{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--BOOK_INFORMATION-->\n",
    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
    "\n",
    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
    "\n",
    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--NAVIGATION-->\n",
    "< [What Is Machine Learning?](05.01-What-Is-Machine-Learning.ipynb) | [Contents](Index.ipynb) | [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb) >\n",
    "\n",
    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.02-Introducing-Scikit-Learn.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introducing Scikit-Learn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are several Python libraries which provide solid implementations of a range of machine learning algorithms.\n",
    "One of the best known is [Scikit-Learn](http://scikit-learn.org), a package that provides efficient versions of a large number of common algorithms.\n",
    "Scikit-Learn is characterized by a clean, uniform, and streamlined API, as well as by very useful and complete online documentation.\n",
    "A benefit of this uniformity is that once you understand the basic use and syntax of Scikit-Learn for one type of model, switching to a new model or algorithm is very straightforward.\n",
    "\n",
    "This section provides an overview of the Scikit-Learn API; a solid understanding of these API elements will form the foundation for understanding the deeper practical discussion of machine learning algorithms and approaches in the following chapters.\n",
    "\n",
    "We will start by covering *data representation* in Scikit-Learn, followed by covering the *Estimator* API, and finally go through a more interesting example of using these tools for exploring a set of images of hand-written digits."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Representation in Scikit-Learn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Machine learning is about creating models from data: for that reason, we'll start by discussing how data can be represented in order to be understood by the computer.\n",
    "The best way to think about data within Scikit-Learn is in terms of tables of data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data as table\n",
    "\n",
    "A basic table is a two-dimensional grid of data, in which the rows represent individual elements of the dataset, and the columns represent quantities related to each of these elements.\n",
    "For example, consider the [Iris dataset](https://en.wikipedia.org/wiki/Iris_flower_data_set), famously analyzed by Ronald Fisher in 1936.\n",
    "We can download this dataset in the form of a Pandas ``DataFrame`` using the [seaborn](http://seaborn.pydata.org/) library:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "iris = sns.load_dataset('iris')\n",
    "iris.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here each row of the data refers to a single observed flower, and the number of rows is the total number of flowers in the dataset.\n",
    "In general, we will refer to the rows of the matrix as *samples*, and the number of rows as ``n_samples``.\n",
    "\n",
    "Likewise, each column of the data refers to a particular quantitative piece of information that describes each sample.\n",
    "In general, we will refer to the columns of the matrix as *features*, and the number of columns as ``n_features``."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Features matrix\n",
    "\n",
    "This table layout makes clear that the information can be thought of as a two-dimensional numerical array or matrix, which we will call the *features matrix*.\n",
    "By convention, this features matrix is often stored in a variable named ``X``.\n",
    "The features matrix is assumed to be two-dimensional, with shape ``[n_samples, n_features]``, and is most often contained in a NumPy array or a Pandas ``DataFrame``, though some Scikit-Learn models also accept SciPy sparse matrices.\n",
    "\n",
    "The samples (i.e., rows) always refer to the individual objects described by the dataset.\n",
    "For example, the sample might be a flower, a person, a document, an image, a sound file, a video, an astronomical object, or anything else you can describe with a set of quantitative measurements.\n",
    "\n",
    "The features (i.e., columns) always refer to the distinct observations that describe each sample in a quantitative manner.\n",
    "Features are generally real-valued, but may be Boolean or discrete-valued in some cases."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Target array\n",
    "\n",
    "In addition to the feature matrix ``X``, we also generally work with a *label* or *target* array, which by convention we will usually call ``y``.\n",
    "The target array is usually one dimensional, with length ``n_samples``, and is generally contained in a NumPy array or Pandas ``Series``.\n",
    "The target array may have continuous numerical values, or discrete classes/labels.\n",
    "While some Scikit-Learn estimators do handle multiple target values in the form of a two-dimensional, ``[n_samples, n_targets]`` target array, we will primarily be working with the common case of a one-dimensional target array.\n",
    "\n",
    "Often one point of confusion is how the target array differs from the other features columns. The distinguishing feature of the target array is that it is usually the quantity we want to *predict from the data*: in statistical terms, it is the dependent variable.\n",
    "For example, in the preceding data we may wish to construct a model that can predict the species of flower based on the other measurements; in this case, the ``species`` column would be considered the target array.\n",
    "\n",
    "With this target array in mind, we can use Seaborn (see [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb)) to conveniently visualize the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\KZCF\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:2065: UserWarning: The `size` parameter has been renamed to `height`; pleaes update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 518.85x432 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import seaborn as sns; sns.set()\n",
    "sns.pairplot(iris, hue='species', size=1.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For use in Scikit-Learn, we will extract the features matrix and target array from the ``DataFrame``, which we can do using some of the Pandas ``DataFrame`` operations discussed in the [Chapter 3](03.00-Introduction-to-Pandas.ipynb):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 4)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_iris = iris.drop('species', axis=1)\n",
    "X_iris.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150,)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_iris = iris['species']\n",
    "y_iris.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To summarize, the expected layout of features and target values is visualized in the following diagram:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](figures/05.02-samples-features.png)\n",
    "[figure source in Appendix](06.00-Figure-Code.ipynb#Features-and-Labels-Grid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With this data properly formatted, we can move on to consider the *estimator* API of Scikit-Learn:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scikit-Learn's Estimator API"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Scikit-Learn API is designed with the following guiding principles in mind, as outlined in the [Scikit-Learn API paper](http://arxiv.org/abs/1309.0238):\n",
    "\n",
    "- *Consistency*: All objects share a common interface drawn from a limited set of methods, with consistent documentation.\n",
    "\n",
    "- *Inspection*: All specified parameter values are exposed as public attributes.\n",
    "\n",
    "- *Limited object hierarchy*: Only algorithms are represented by Python classes; datasets are represented\n",
    "  in standard formats (NumPy arrays, Pandas ``DataFrame``s, SciPy sparse matrices) and parameter\n",
    "  names use standard Python strings.\n",
    "\n",
    "- *Composition*: Many machine learning tasks can be expressed as sequences of more fundamental algorithms,\n",
    "  and Scikit-Learn makes use of this wherever possible.\n",
    "\n",
    "- *Sensible defaults*: When models require user-specified parameters, the library defines an appropriate default value.\n",
    "\n",
    "In practice, these principles make Scikit-Learn very easy to use, once the basic principles are understood.\n",
    "Every machine learning algorithm in Scikit-Learn is implemented via the Estimator API, which provides a consistent interface for a wide range of machine learning applications."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basics of the API\n",
    "\n",
    "Most commonly, the steps in using the Scikit-Learn estimator API are as follows\n",
    "(we will step through a handful of detailed examples in the sections that follow).\n",
    "\n",
    "1. Choose a class of model by importing the appropriate estimator class from Scikit-Learn.\n",
    "2. Choose model hyperparameters by instantiating this class with desired values.\n",
    "3. Arrange data into a features matrix and target vector following the discussion above.\n",
    "4. Fit the model to your data by calling the ``fit()`` method of the model instance.\n",
    "5. Apply the Model to new data:\n",
    "   - For supervised learning, often we predict labels for unknown data using the ``predict()`` method.\n",
    "   - For unsupervised learning, we often transform or infer properties of the data using the ``transform()`` or ``predict()`` method.\n",
    "\n",
    "We will now step through several simple examples of applying supervised and unsupervised learning methods."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Supervised learning example: Simple linear regression\n",
    "\n",
    "As an example of this process, let's consider a simple linear regression—that is, the common case of fitting a line to $(x, y)$ data.\n",
    "We will use the following simple data for our regression example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "rng = np.random.RandomState(42)\n",
    "x = 10 * rng.rand(50)\n",
    "y = 2 * x - 1 + rng.randn(50)\n",
    "plt.scatter(x, y);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With this data in place, we can use the recipe outlined earlier. Let's walk through the process: "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Choose a class of model\n",
    "\n",
    "In Scikit-Learn, every class of model is represented by a Python class.\n",
    "So, for example, if we would like to compute a simple linear regression model, we can import the linear regression class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that other more general linear regression models exist as well; you can read more about them in the [``sklearn.linear_model`` module documentation](http://Scikit-Learn.org/stable/modules/linear_model.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Choose model hyperparameters\n",
    "\n",
    "An important point is that *a class of model is not the same as an instance of a model*.\n",
    "\n",
    "Once we have decided on our model class, there are still some options open to us.\n",
    "Depending on the model class we are working with, we might need to answer one or more questions like the following:\n",
    "\n",
    "- Would we like to fit for the offset (i.e., *y*-intercept)?\n",
    "- Would we like the model to be normalized?\n",
    "- Would we like to preprocess our features to add model flexibility?\n",
    "- What degree of regularization would we like to use in our model?\n",
    "- How many model components would we like to use?\n",
    "\n",
    "These are examples of the important choices that must be made *once the model class is selected*.\n",
    "These choices are often represented as *hyperparameters*, or parameters that must be set before the model is fit to data.\n",
    "In Scikit-Learn, hyperparameters are chosen by passing values at model instantiation.\n",
    "We will explore how you can quantitatively motivate the choice of hyperparameters in [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb).\n",
    "\n",
    "For our linear regression example, we can instantiate the ``LinearRegression`` class and specify that we would like to fit the intercept using the ``fit_intercept`` hyperparameter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LinearRegression(fit_intercept=True)\n",
    "model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Keep in mind that when the model is instantiated, the only action is the storing of these hyperparameter values.\n",
    "In particular, we have not yet applied the model to any data: the Scikit-Learn API makes very clear the distinction between *choice of model* and *application of model to data*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Arrange data into a features matrix and target vector\n",
    "\n",
    "Previously we detailed the Scikit-Learn data representation, which requires a two-dimensional features matrix and a one-dimensional target array.\n",
    "Here our target variable ``y`` is already in the correct form (a length-``n_samples`` array), but we need to massage the data ``x`` to make it a matrix of size ``[n_samples, n_features]``.\n",
    "In this case, this amounts to a simple reshaping of the one-dimensional array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50, 1)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = x[:, np.newaxis]\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4. Fit the model to your data\n",
    "\n",
    "Now it is time to apply our model to data.\n",
    "This can be done with the ``fit()`` method of the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This ``fit()`` command causes a number of model-dependent internal computations to take place, and the results of these computations are stored in model-specific attributes that the user can explore.\n",
    "In Scikit-Learn, by convention all model parameters that were learned during the ``fit()`` process have trailing underscores; for example in this linear model, we have the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.9776566])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.9033107255311164"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.intercept_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These two parameters represent the slope and intercept of the simple linear fit to the data.\n",
    "Comparing to the data definition, we see that they are very close to the input slope of 2 and intercept of -1.\n",
    "\n",
    "One question that frequently comes up regards the uncertainty in such internal model parameters.\n",
    "In general, Scikit-Learn does not provide tools to draw conclusions from internal model parameters themselves: interpreting model parameters is much more a *statistical modeling* question than a *machine learning* question.\n",
    "Machine learning rather focuses on what the model *predicts*.\n",
    "If you would like to dive into the meaning of fit parameters within the model, other tools are available, including the [Statsmodels Python package](http://statsmodels.sourceforge.net/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5. Predict labels for unknown data\n",
    "\n",
    "Once the model is trained, the main task of supervised machine learning is to evaluate it based on what it says about new data that was not part of the training set.\n",
    "In Scikit-Learn, this can be done using the ``predict()`` method.\n",
    "For the sake of this example, our \"new data\" will be a grid of *x* values, and we will ask what *y* values the model predicts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "xfit = np.linspace(-1, 11)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As before, we need to coerce these *x* values into a ``[n_samples, n_features]`` features matrix, after which we can feed it to the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xfit = xfit[:, np.newaxis]\n",
    "yfit = model.predict(Xfit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let's visualize the results by plotting first the raw data, and then this model fit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x, y)\n",
    "plt.plot(xfit, yfit);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Typically the efficacy of the model is evaluated by comparing its results to some known baseline, as we will see in the next example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Supervised learning example: Iris classification\n",
    "\n",
    "Let's take a look at another example of this process, using the Iris dataset we discussed earlier.\n",
    "Our question will be this: given a model trained on a portion of the Iris data, how well can we predict the remaining labels?\n",
    "\n",
    "For this task, we will use an extremely simple generative model known as Gaussian naive Bayes, which proceeds by assuming each class is drawn from an axis-aligned Gaussian distribution (see [In Depth: Naive Bayes Classification](05.05-Naive-Bayes.ipynb) for more details).\n",
    "Because it is so fast and has no hyperparameters to choose, Gaussian naive Bayes is often a good model to use as a baseline classification, before exploring whether improvements can be found through more sophisticated models.\n",
    "\n",
    "We would like to evaluate the model on data it has not seen before, and so we will split the data into a *training set* and a *testing set*.\n",
    "This could be done by hand, but it is more convenient to use the ``train_test_split`` utility function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from sklearn.cross_validation import train_test_split\n",
    "from sklearn.model_selection import train_test_split\n",
    "Xtrain, Xtest, ytrain, ytest = train_test_split(X_iris, y_iris,\n",
    "                                                random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the data arranged, we can follow our recipe to predict the labels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB # 1. choose model class\n",
    "model = GaussianNB()                       # 2. instantiate model\n",
    "model.fit(Xtrain, ytrain)                  # 3. fit model to data\n",
    "y_model = model.predict(Xtest)             # 4. predict on new data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can use the ``accuracy_score`` utility to see the fraction of predicted labels that match their true value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9736842105263158"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(ytest, y_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With an accuracy topping 97%, we see that even this very naive classification algorithm is effective for this particular dataset!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unsupervised learning example: Iris dimensionality\n",
    "\n",
    "As an example of an unsupervised learning problem, let's take a look at reducing the dimensionality of the Iris data so as to more easily visualize it.\n",
    "Recall that the Iris data is four dimensional: there are four features recorded for each sample.\n",
    "\n",
    "The task of dimensionality reduction is to ask whether there is a suitable lower-dimensional representation that retains the essential features of the data.\n",
    "Often dimensionality reduction is used as an aid to visualizing data: after all, it is much easier to plot data in two dimensions than in four dimensions or higher!\n",
    "\n",
    "Here we will use principal component analysis (PCA; see [In Depth: Principal Component Analysis](05.09-Principal-Component-Analysis.ipynb)), which is a fast linear dimensionality reduction technique.\n",
    "We will ask the model to return two components—that is, a two-dimensional representation of the data.\n",
    "\n",
    "Following the sequence of steps outlined earlier, we have:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA  # 1. Choose the model class\n",
    "model = PCA(n_components=2)            # 2. Instantiate the model with hyperparameters\n",
    "model.fit(X_iris)                      # 3. Fit to data. Notice y is not specified!\n",
    "X_2D = model.transform(X_iris)         # 4. Transform the data to two dimensions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's plot the results. A quick way to do this is to insert the results into the original Iris ``DataFrame``, and use Seaborn's ``lmplot`` to show the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 446.85x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris['PCA1'] = X_2D[:, 0]\n",
    "iris['PCA2'] = X_2D[:, 1]\n",
    "sns.lmplot(\"PCA1\", \"PCA2\", hue='species', data=iris, fit_reg=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that in the two-dimensional representation, the species are fairly well separated, even though the PCA algorithm had no knowledge of the species labels!\n",
    "This indicates to us that a relatively straightforward classification will probably be effective on the dataset, as we saw before."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unsupervised learning: Iris clustering\n",
    "\n",
    "Let's next look at applying clustering to the Iris data.\n",
    "A clustering algorithm attempts to find distinct groups of data without reference to any labels.\n",
    "Here we will use a powerful clustering method called a Gaussian mixture model (GMM), discussed in more detail in [In Depth: Gaussian Mixture Models](05.12-Gaussian-Mixtures.ipynb).\n",
    "A GMM attempts to model the data as a collection of Gaussian blobs.\n",
    "\n",
    "We can fit the Gaussian mixture model as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "cannot import name 'GMM' from 'sklearn.mixture' (C:\\Users\\KZCF\\Anaconda3\\lib\\site-packages\\sklearn\\mixture\\__init__.py)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-24-df85f2dd883f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmixture\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mGMM\u001b[0m      \u001b[1;31m# 1. Choose the model class\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m model = GMM(n_components=3,\n\u001b[0;32m      3\u001b[0m             covariance_type='full')  # 2. Instantiate the model with hyperparameters\n\u001b[0;32m      4\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_iris\u001b[0m\u001b[1;33m)\u001b[0m                    \u001b[1;31m# 3. Fit to data. Notice y is not specified!\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0my_gmm\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_iris\u001b[0m\u001b[1;33m)\u001b[0m        \u001b[1;31m# 4. Determine cluster labels\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mImportError\u001b[0m: cannot import name 'GMM' from 'sklearn.mixture' (C:\\Users\\KZCF\\Anaconda3\\lib\\site-packages\\sklearn\\mixture\\__init__.py)"
     ]
    }
   ],
   "source": [
    "from sklearn.mixture import GMM      # 1. Choose the model class\n",
    "model = GMM(n_components=3,\n",
    "            covariance_type='full')  # 2. Instantiate the model with hyperparameters\n",
    "model.fit(X_iris)                    # 3. Fit to data. Notice y is not specified!\n",
    "y_gmm = model.predict(X_iris)        # 4. Determine cluster labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.mixture import GaussianMixture      # 1. Choose the model class\n",
    "model = GaussianMixture(n_components=3,\n",
    "            covariance_type='full')  # 2. Instantiate the model with hyperparameters\n",
    "model.fit(X_iris)                    # 3. Fit to data. Notice y is not specified!\n",
    "y_gmm = model.predict(X_iris)        # 4. Determine cluster labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As before, we will add the cluster label to the Iris ``DataFrame`` and use Seaborn to plot the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1166.85x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris['cluster'] = y_gmm\n",
    "sns.lmplot(\"PCA1\", \"PCA2\", data=iris, hue='species',\n",
    "           col='cluster', fit_reg=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By splitting the data by cluster number, we see exactly how well the GMM algorithm has recovered the underlying label: the *setosa* species is separated perfectly within cluster 0, while there remains a small amount of mixing between *versicolor* and *virginica*.\n",
    "This means that even without an expert to tell us the species labels of the individual flowers, the measurements of these flowers are distinct enough that we could *automatically* identify the presence of these different groups of species with a simple clustering algorithm!\n",
    "This sort of algorithm might further give experts in the field clues as to the relationship between the samples they are observing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Application: Exploring Hand-written Digits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To demonstrate these principles on a more interesting problem, let's consider one piece of the optical character recognition problem: the identification of hand-written digits.\n",
    "In the wild, this problem involves both locating and identifying characters in an image. Here we'll take a shortcut and use Scikit-Learn's set of pre-formatted digits, which is built into the library."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading and visualizing the digits data\n",
    "\n",
    "We'll use Scikit-Learn's data access interface and take a look at this data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import load_digits\n",
    "digits = load_digits()\n",
    "digits.images.shape\n",
    "digits.images[0]\n",
    "digits.target[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The images data is a three-dimensional array: 1,797 samples each consisting of an 8 × 8 grid of pixels.\n",
    "Let's visualize the first hundred of these:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAdcAAAHNCAYAAABMyTE8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzsnXtclVXa/q8NiKCIgoqIeEDG8ZB5Pr2maVY2MilmVDaSpZl49rWwtF6znOkz2ZiOiqVWoo5+Rs0JeEtetSmzg2nKiOmoKWYYohZCgIKAsH9/+JPXcV1sFuy197OZ9/7+FXf7cD3ruZ+1fJ517fu22e12OwRBEARBMIaX1QIEQRAE4d8NWVwFQRAEwTCyuAqCIAiCYWRxFQRBEATDyOIqCIIgCIaRxVUQBEEQDCOLqyAIgiAYRhZXQRAEQTCMLK6CIAiCYBhZXAVBEATBMLK4CoIgCIJhZHEVBEEQBMPI4ioIgiAIhvFx5Yfv2LFDib3++utKbPDgwUrshRdeUGKNGzc2I6yGjB07VokVFBQosf/8z/9UYsOHD3eJpurYv3+/Eps8ebIS69KlixLbsmWLSzTdyttvv63EFi9erMRat26txD766CMlZlVusDyIj49XYmvXrnWHHAWWu+Hh4UpsyZIl7pBTa3SvwdTUVHfIoaxbt06JMY27d+9WYsePH1digYGBSuzLL7+s/G+bzYZGjRrVVOa/sGjRIi19MTExSmzixIlKjGk2DZvH2Di7Yx5zhEsX16KiIiV2/vx5JXb58mUlVlFR4RJNteHixYtK7JdfflFixcXF7pCjxbVr15TYuXPnlFhwcLA75CiwiyEzM1OJeXmpD1c8KTeYlp9++skCJRyWu35+fhYocQ7da9BKWE4zjdnZ2UqM5T77B+Ot+caujZqSm5urxLKyspQYOw6rrkN2fXlaLgDyWFgQBEEQjCOLqyAIgiAYxqWPhdm+6dmzZ5VYXl6eEmOPK7dt26bEHnnkkVqq06dJkyZKbO/evUpsz549Siw6Otolmm4lPT1did1zzz1KjD1m+uGHH1wh6V+YN2+eEmPncs2aNUosLi5OiaWlpSmx++67r5bqnGP9+vVKrEePHu4XUgXs/LLc3bBhgxJr27at1ueZJiUlRYkxzQsXLnS5Fmdhc8ef//xnrRh71Mk+zxnY3MFgef7ZZ59pxZyB5RvLD4bNZlNi3bt3V2K6Y1BT5M5VEARBEAwji6sgCIIgGEYWV0EQBEEwjCyugiAIgmAYY4YmZjJh5qUzZ84osfbt2yux+++/X+s7TBua2Oa27ia9VUaW5ORkJcY27kePHq3EXn31VZdouhX2o29mduvdu7cSi4iIUGJWmZeYwYQZPVgxEV0jULt27WqoyjHMAKP7m8qhQ4cqMXeYbHSNSiyfrYSdd8Yrr7yixFh+mDYHMdicxXKQ5Tk770wzyyNddH+/OmTIECXGjsMdY3oTuXMVBEEQBMPI4ioIgiAIhpHFVRAEQRAMI4urIAiCIBjGmKGJVVnq1auXEmPmJQYzt5iGVUVhZoP8/Hytz3Nm494ZmJGCbeaz17mjghQ7599//70SYwY4Zl5iuRYUFFRLdfowUwczojz11FNKjI09M4Sw/HMGlgdHjhxRYizHmdnFtHmJwUwszKBnZSUsZ6oTsXmHwYyKLLecgX1ez549lRjLc5YLpg15up/HxooZ3txZ4F/uXAVBEATBMLK4CoIgCIJhZHEVBEEQBMPI4ioIgiAIhnGpoYlVWXLm80ybVpjJhG3w636vOzbL2XcwgwTb4Gcwk447YCan3NxcJcYMTSz297//XYk5ky+srdWcOXOU2JNPPqn1ecuXL1diiYmJNRdWQ1geMOMNq0zGjpehW5lIF5bjzNjC8p6ZWEybbKr6TGequ7Hz5A6DpO6cxVr+MQOiOyqMMXMbu9Znz56txNg5YmYtE8chd66CIAiCYBhZXAVBEATBMLK4CoIgCIJhZHEVBEEQBMMYMzSxDWXWIo7BzEuHDh1SYo8++mjNhbkRtlluuooMq+DDzDIMZppwR8UdXVgOMaNSXFycElu8eLESe/3112uthbVgY7ENGzYoMZYHDKtapjljlNFtnecMzEzCDDXMjMNMWIcPH1Zizl6XTCO7vmw2m9br3GFeYnl5zz33KDHW8o+dd5a/7NhMm5zYcTgz9zJDnq4h1BFy5yoIgiAIhpHFVRAEQRAMI4urIAiCIBhGFldBEARBMIwRQ9OOUzvw3D+fQ2lFKTo16YQ//scf0ci3ETUlvf/++1oxxgsvvOC01lux2+14KuUp3BlyJ+IHxhv9bFew6dtNSG2bChts8PP2w/N3Po87gu6gVWBYWzFmQGAt5yZMmKD1Oh0SvknA24fehg02RAZH4p2R7yCkYQjmzZunvFa3vdzHH3+sxEyb3YYOHYrkk8l4IukJFM4vBMANNMxIwcwprJKTSTPZc7uew/vH30ewfzAAoGOzjtgas5VWmmLGLN1WdyZNWEcvHcXM/5mJ/JJ8eNu8sebBNegd1ptWSWNGJWaUYcYbZk6pjaFp45GNWPr10sq/80vykVWQhaw5WWgR0IIaY9hYDxkypMbf7QxJJ5Kw8LOFsFfYEeQXhOX3LkdEk4gq9bHjYOPKWtOxim+1aaO48sBKJBxMgL+PPzo374xVUasqc5vBzic7DqbPhHmJ4fSd689Xf8aElAlYNWQV/h79d7Ru1Bp/OvwnE9pcyomfT+Dejfdi+/HtVkvR4ruc7zD347lY9R+rsPWerZjUcRLiD3r2PwjSstOwZN8S7Ju4D8emHUOH4A5Y8OkCq2VpcfryacTvjofdbrdaihb7svZhS8wWpE9JR/qUdGyN2Wq1JIcUlRVh+KbheP6u53E47jAW3L0A4z4YZ7Ush4zvPr5yfA8+cxChAaFIGJGAFgEtrJZWJcVlxYhNisUHj32AL8Z9gd9E/AYv7DV7k2KaPWf3YPFXi/HJ+E+QPiUdUb+KwuQPJ1stq8Y4vbjuPrMbfVv1RUTgjX8Jjfv1OKScTfH4SWnVwVWY1GsSHunyiNVStKjvUx/vjnwXzf2aAwDuaHIHcq7loKyizGJlVdM7rDdOzzyNxn6Nce36NZwvPI+mDZpaLataisqKEJsUi6UPLK3+xR5AyfUSHL5wGG989QbufPtOPLztYZzLP2e1LIfsPrMbkUGRiOoQBQAY1XEUtj2yzWJV+iz+ajFCGoYgro/6szBPotxeDrvdjvxr+QCAq2VX4eftZ7Eqx6RdSMN97e9DeGA4AGBM5zH48NSHKC0vtVhZzXB6cf2x4Ee0Dmxd+Xdog1BcKbuCK2VXnP1ol5IQlYDf3fk7q2Vo065JO/z2178FcONx9pvH3sSQ0CGo51XPYmWOqeddD8knkxG+NByfZ36OCT3UR86eRtxHcYjrHYduLbpZLUWL7MJsDIsYhj8M+wO+nfItBrQagOgt0R79D9xTl08hNCAUT6c8jT5r++D+v9yP6xXXrZalRU5RDt78+k0se2CZ1VKqJcA3AKsfXI2B6wai87ud8c6Rd/DKoFesluWQ/q3649OznyLzl0wAQGJ6IkrLS3G56LLFymqG04trhb0CNqg/lPa2eTv70QKh+Hoxnj/0PH68+iMW9lR/7O2JjO40GjnP5+CVIa/ggU0PoMJeYbWkKnnr4Fvw8fLBxJ4TrZaiTURQBFLHpaJrSFfYbDbED4zHmdwz+OGXH6yWViVl5WVIPZ2Kyb0n49DkQ5jZbyaiNkeh5HqJ1dKqZW3aWkR3jEb7ILWzk6dx9NJRLNq7CMenHceJSSfwXL/nMH7HeI/+h9fgtoOxcMhCPLT1IfRZ2wdeNi8E+wfD19vXamk1wmlDU5vGbXDg/IHKFmKZv2QiyC8IXTt2pVVzmCmpT58+Sky3upNpmMmEmXmYUYQZi5g5o7acyz+HKYemoHOzzvjvp/4b/vX8AehXLGHGAnYczChSG0NTRm4GLl65iEFtBgEAJvaciCk7piCvOI9WY5o8WW9fhZmX1qxZU2N9jPXp61FUVoQeq3ugtLwUxdeL0WN1D6SOS0VYozDl9Sxf8vPzlZjJPLidby99iyMXj+CJ7k9Uxuywo553PezZs0d5vW5FL2bCMlVJKKxRGDo374z+4f0BANGdojHpw0n4Pu97OlbMUMPMKUyf6UpYW/+5FSt+s0KJs+ufVfByZ1W0XWd24a42dyEyOBIAEH93PF78/EWU1y9HswbN6Hixa5MZn9icYKL9YGFJIYa0G4Knez0NADhfcB4L9iyoNDSx72DzHTMgsnNkuoreTZy+cx0eORz7s/bj9OXTAIDVh1YjulPtnKVC1RSWFGLo+qEY02kMtsRsqVxYPZkLhRcwdvtY5BTlAAA2H92MriFdPXrf9ZtnvsGxaceQPiUdqeNS4e/jj/Qp6XRh9RS8bF6YtXMWzubd6K/59qG30a1Ft8o9K09kRIcROJt3FmnZN/4R/Xnm57DBhoigCIuVOSavOA8ZuRkY2Hqg1VK06NWyF/b+sBeXrlwCACSfTEZEkwg0a9DMYmVVk12YjaHrh6KgpAAA8NoXr+Hxro/TUpKejNN3riENQ5AYnYiY92NQWl6KyKBIbHxoowltwi0kfJOAzPxMJJ1MQtLJpMr4J+M/8djFanDbwXhp8EsYun4ofLx8ENYoDMmPucb2/n+ZriFdsXLESoz860iU28sRHhiOvz78V6tlOSQ0IBTJY5MxLXUarpZeRX2f+vjgsQ/g5+OHa7hmtbwqycjNQMuAlqjn7dleh5sMixiGuQPnYuiGofD19kWwfzBSxqpPqzyJjs06Yt6geej/bn9U2CswqPUgJEQlWC2rxhj5nWtUh6hK119dY/3o9VZL0GL+4PmYP3i+1TJqzNS+UzG171SrZdSKdk3a4cqLnm3Mu0lst1jEdou1WkaNuLvt3Tgw6YDVMmpE31Z9kTErw2oZNWJ6v+mY3m+61TJqxIx+MzCj3wyrZTiFsa44jICAACXWqlUrJda8eXNXynCakJAQJda2bVsl1qyZ5zxq8fVVN/91jyM4uOofa5siMDBQibHcYDRt6jl36j4+6iXExtTPz5qfP7BzyfQxrMpnLy91t4odR5s2bZQYy3F2LbiCsDB168Df37O3b3TnBHa9sveyc2calguhoaFKrEGDBkrMXbkAADa7J9vGBEEQBKEOIrWFBUEQBMEwsrgKgiAIgmFkcRUEQRAEw8jiKgiCIAiGcalbuKCgQIn9+c9/VmLbt6udaQYMGKDE1q5da0aYAQYNGqTEmKNuy5YtWq9zht27dyuxdevWKTE2fqa1MLKyspQY08fygOkbPny4EouJiVFiXbp00ZVYa1g+s2P78ssvlZhVecCuy+PHj2t9BzuO8HDXF6vwpHGuCjauTCOLsZxesmSJGWEOiI9XO2ux60Z3jn755ZfNCHMA08Lyg40f0+wqXLq4VlSoNWRzc3OV2LlzagePm+UUPRW2YLCyZmwMTFNcXKzELl68aIkWxvXrakF23TxgY8reW1pqTccMVmKNHYcn5QHTnJmZqfUd7Fy6A08a56pg362rOycnxyWaqoN9L/tHQnZ2thJj16E7uHJF/e05m4+vXbO2GIk8FhYEQRAEw8jiKgiCIAiGceljYdbdgnVhWbhQbZ3GOl6wmCu7jdyEaWaP0ViMPRYy3RWDdS9h38HGz0QXi+pgHU1YdwqmhY0f6+rCjtd0twumhY0p6yqk+3nO5EZiYqIS27t3rxJjHU7YNcg6pugem2lYvrCxclfHGdaFRbebD9PIjs8dMC3s2JyZT0znTHKyWp+czb26XZNchdy5CoIgCIJhZHEVBEEQBMPI4ioIgiAIhpHFVRAEQRAMY8zQxDbumRGImW9eeeUVJcbMHmyj3R3Mnj1b63VDhgxRYu4wgLDvYAaJ0aNHKzF3GJqYiYCdS2ZAYLnBDDns2Eyja7hihgt2jti4sPfqwgxcbJzZ69ixucscdDtMMzNmLVu2zB1yKMxAozv+usYnd8CuG1aQgeUvyw93zHe647xhwwYlxuYTV2mWO1dBEARBMIwsroIgCIJgGFlcBUEQBMEwsrgKgiAIgmGMGZp0zQ+6FZXcYaZgZhRm7NAtau4OmPGBbfCz8bPKNKGLrpmHmRdMmxKYqYMZJJiphmnJz89XYqYrSDF0K4kxLVbli65x0R0mtqqIjo5WYm3btlVizNTJ8pwdCxt/03nOzruuEZUZEN0Bm6OZgZONFXuvMyZCR8idqyAIgiAYRhZXQRAEQTCMLK6CIAiCYBhZXAVBEATBMMYMTVZVT3IGZhhgMWZU0DWFmIZt0rOqIwyrWuLpwkxEutWETJsSdM08zNTBjoPRs2fPGiiqHmfafU2YMMGoFmdgOcmIiIhQYt27d1dir776qhJjhiRnceZ8MrOcbqtGZ2BGKjaGzIhq1TzhTMs+dry6Vcxqity5CoIgCIJhZHEVBEEQBMPI4ioIgiAIhpHFVRAEQRAMY7Pb7XYTH8RMCEFBQUqMGU9Yqza2gc6MO+4wEbGKJWxjnLVC0zVnmIYZbXRbpnkSuhWpWF6xlm666FbvYt/LqjExU5xVFZB08/nw4cNKzB3XGzOssDHVbQXJzpGzY8/ygxnZmNGGfTeb79g5MT3+um0PmRbT5ip3wHI/MTFRiZkwSMqdqyAIgiAYRhZXQRAEQTCMLK6CIAiCYBhZXAVBEATBMEYqNO04tQPzP5mPkvISdGvRDe+Neg+B9QOpUYm16EpKSlJizNRgejPfbrfjqZSncGfInYgfGF/l65hRieHqiiWbvt2EP+37E2ywoUG9BlgxYgX6hPWhRpvly5crMXYc7L3sOG43XPj4+CA8PLxazQnfJGDVgVWADYhoHIHl9y5H8wbNsXfvXuW1eXl5SoyZRJi5xbQ5qEmTJkg+mYwnkp5A4fxCANwkpmvkc8ZcpcNzu57D1mNbEeR347s7BHXAuqh1dJxZ+zBWlcfV5qWjl45i5v/MRH5JPrxt3ljz4Br0DutNjTK67eV0r4Xb80Unnzce2YilXy+t/Du/JB9ZBVnImpOFFgEtqOGSGZVYzuhWWasNSSeSsPCzhbBX2BHkF4Tl9y5HRJMb1a1YBS+mz93mu5UHViLhYAL8ffzRuXlnrIpahWD/4Cr16VYIPHv2rBJjJqfa5MftOH3n+vPVnzEhZQL+9ujf8N2M79C+SXvM+/s8Zz/W5Zz4+QTu3Xgvth/fbrUULb7L+Q5zP56LneN2In1KOv7r7v/CmK1jrJblkLTsNCzZtwQ7H92Jr2O/RmSTSLz29WtWy9Li9OXTiN8dD0NmepezL2sf3hvxHr4Y9wW+GPcF1kWts1qSQ4rKijB803A8f9fzOBx3GAvuXoBxH4yzWpZDxncfj/Qp6Uifko6DzxxEaEAoEkYkoEVAC6ulVUlxWTFik2LxwWMf4ItxX+A3Eb/BC3tfsFqWQ/ac3YPFXy3GJ+M/QfqUdET9KgqTP5xstawa4/TiuvvMbvRt1RcdmnYAAEztOxWbj272+Elp1cFVmNRrEh7p8ojVUrSo71Mf7458Fy0btQQA9Anrg4tXLqK0vNRiZVXTO6w3Ts88jcb1G+Pa9WvIvpKNYL9gq2VVS1FZEWKTYrH0gaXVv9gDKLlegsMXDmNF2goM3DQQ4z8ajx8LfrRalkN2n9mNyKBIRHWIAgCM6jgK2x7ZZrEqfRZ/tRghDUMQ1yfOaikOKbeXw263I//ajac9V8uuws/bz2JVjkm7kIb72t+H8MAbd4pjOo/Bh6c+9Oi5juH0Y+EfC35E68DWlX+HB4ajoKQAhaWFzn60S0mISgBw4yKvC7Rr0g7tmrQDcONx9rO7nsWojqPg6+1rrbBqqOddDzvO7MCsv89Cfe/6eHHAi1ZLqpa4j+IQ1zsO3Vp0s1qKFtmF2RgWMQwv9X8JnZt2xsp/rMS4j8Zh7+PqI2FP4dTlUwgNCMXTKU/jyKUjaOLXBG/c/4bVsrTIKcrBm1+/ibTJaVZLqZYA3wCsfnA1Bq4biGC/YJRXlGPnozutluWQ/q36Y8WBFcj8JRNtm7RFYnoiSstLcbnocuXNRV3A6TvXCnsFbLApcW+bt7MfLRCull7Fo9sfRUZuBt4d9a7VcrT4beRvcSbuDF4Y8AIeTn4YFfYKqyVVyVsH34KPlw8m9pxotRRtIoIikDouFV2adYHNZsPMXjPxQ/4POFdwzmppVVJWXobU06mY3HsyDk0+hJn9ZiJqcxRKrpdYLa1a1qatRXTHaLQPam+1lGo5eukoFu1dhOPTjuPEpBN4rt9zGL9jvEc/WRzcdjAWDlmIh7Y+hD5r+8DL5oVg/2CPv5G4HafvXNs0boMD5w9U/n2+4DyC/ILQ0LchrXLBDAdsM5qZR6yCGTuYAeTIkSNKzGRLt3P55zDyryPRuVln7HlyD/zr+QPgpglnKhsxfbcbcvz8/Krd4M/IzcDFKxcRXnHjdQ+GPYhnC59F5qVMamzThbULY2NQG9anr0dRWRF6rO6B0vJSFF8vRo/VPZA6LhVhjcKU17N8ZsYxU/oY3176FkcuHkHrvBtPkOx2O66XX8epk6fwzJPPKK9nhjDTLfuqI6xRGDo374z+4f0BANGdojHpw0n4Pu977TaDzATEzEssX3Rb8TG2/nMrVvxmhdZr2fXvanPbrew6swt3tbkLkcGRAID4u+Px4ucvorx+OZo1aKatj42rqygsKcSQdkPwdK+nAdxYUxbsWVBpaGJrw5w5c7Q+m83bLD9unwO9vGp+H+r0nevwyOHYn7Ufpy+fBgCsPrQa0Z3M90r8v05hSSGGrh+KMZ3GYEvMlsqF1ZO5UHgBY7ePRe61XABA8tlk/LrJrysdrZ7IN898g2PTjiF9SjpSx6XC38cf6VPS6cLqKXjZvDBr5yxcKL4AAEjJTkH7hu3RvH5zi5VVzYgOI3A27yzSsm88Wv0883PYYENEkNqj1ZPIK85DRm4GBrYeaLUULXq17IW9P+zFpSuXAADJJ5MR0SQCzRo0s1hZ1WQXZmPo+qEoKCkAALz2xWt4vOvjsNnUJ6SejNN3riENQ5AYnYiY92NQWl6KyKBIbHxoowltwi0kfJOAzPxMJJ1MQtLJ//3p0ifjP7FQlWMGtx2Mlwa/hMd3PQ5vmzdaNGiBtfestVrWvx1dQ7pi5YiVeHHXi6hABZr7NseCzgusluWQ0IBQJI9NxrTUabhaehX1ferjg8c+gJ+PZ5ttMnIz0DKgJep517NaihbDIoZh7sC5GLphKHy9fRHsH4yUsepPTzyJjs06Yt6geej/bn9U2CswqPWgSo9MXcLI71yjOkRVuv7qGutHr7daghbzB8/H/MHz6f/7EZ7rDJ3adypGNB9htYxa0a5JO1x58YrVMrSI7RaL8Nya/Q7Pau5uezcOTDpQ/Qs9iL6t+iJjVobVMmrE9H7TMb3fdKtl1IgZ/WZgRr8ZVstwCiOLa1Ww59TNmqmPI8LC1Eduvr6es3nNjoNpZvsXtXlWX1PYWIWEhCgxtpfKjiMwMFCJ+fn96x1F/fr1tfX5+KhpFhoaqvXeBg0aKDF2bFbB8rlNmzZK7PbxcwXsO5iWgoICJcbOkSfBclL32KzMF/bdwcGe83M0po95Kfz9PWcbiuUC6zrFYPMdG4Pb5+3aPJI21nJOEARBEIQbSG1hQRAEQTCMLK6CIAiCYBhZXAVBEATBMLK4CoIgCIJhXGoRPH78uBKLj1dbuzF32oABA5TYxInWlKTLyspSYoMGDdJ675dffqnEatq6qDpYWzYWW7tW/Y3p8OHDjWphsIpAq1evVmJffPGFEjt27JgSY27Bt956S4npniPTjB07VoktWbJEiZnOA10t7NpisHF2xzXIHL/sONjrWI536dLFjLBawK7D7dv1OnFt2bJFiZnOGTYfs3Fl5103j0yzaNEiJbZ//34lFhMTo8TcuYa4dHEtLVW7GGRnZysx9lMSdoKt4vr160osMzOz1u81DfsJENNXXFzsci2Migq1ljDr3XrhwgUlxo6D/aTo2rVrtVRnnosXLyoxd+QBg2lh+eJJsHxh8wY7DjbnWAnTyP6xznBHzuTk5CgxptmTrq/c3FwlxvLD6jVEHgsLgiAIgmFkcRUEQRAEw7j0sTDrWsE6x7BYSopa/3L06NFKzJnuFrqwDjNWwR7ZsI4mrNMDGz931BD5/vvvlVhamtoL8/7779eKffzxx0rshRde0PoO07AOHSxfatsJqSaw7lJ796o9XVmM5Ys7u7fcCtunZHME63DijnGuCWx+YuOq20HMHd2LPvvsMyWmOwey95o+JyzPWX6wTjnuXEPkzlUQBEEQDCOLqyAIgiAYRhZXQRAEQTCMLK6CIAiCYBhjhia2kc1MSbNnz1ZizPjUo0cPE7IcwsxBbLOc6WMMGTJEiZneLGfmADZWzGjDXseO1/TY9+7dW4kxUxKDmaG2bdumxOLi4mourIawHJ8wYYISW7ZsmRJjJh3dvNKF5QZrxcXOuVVGIGaUefXVV7Xey3LcHQbHmvDUU09pxZhud5wTZwxX7L0st9xhjGPmNmZyYtcwOx8mkDtXQRAEQTCMLK6CIAiCYBhZXAVBEATBMLK4CoIgCIJhXFqhicGMHQzdwvjOwAwRrKqHp8MquTCzFtvg9yQDCDMvRUZGKrFevXopscmTJ7tE062wcWYGPfY6m82mxNjYO2OuYOeXoVuhyR3oNhFwh1mwJjAjFptPmMFHt9GGO45P10zKzFW6ZijTsGuEGQsZ7HjF0CQIgiAIdQRZXAVBEATBMLK4CoIgCIJhZHEVBEEQBMMYMzTpbmSzzXy2Wc4MDMww4EyVG2Y8YcfBTFgbNmxQYla1pmMGBHZsbEw9qUVX+/btlVhERIQSmzdvnhILCgoyqoXlGjMMsbFnba0Ypo0UrEoNu96YPmbM0jUfOoOucZFdW1Yas1h+6FaW0sX0tclygeWgrjGOmbXcgW7VK5YzbD5xVZU6uXMVBEEQBMPI4ioIgiAIhpHFVRAEQRAMI4urIAiCIBjGZrfb7a76cLYhzzaemXFC16Tjquoat8JMU8y8wAxDrMWRaXTbPDETllWVeXRhreRYuzpW3ck0rIViUlKSEmPng5lEXHjjAXvaAAAgAElEQVTpOUTX6HH27FklZrpqEBurnj17Gv2OxMREJeaOeaMqmKGMzRPsPDljcmKGJnY+mRZm4GKfx17nSehWszNxHHLnKgiCIAiGkcVVEARBEAwji6sgCIIgGEYWV0EQBEEwjJEKTTtO7cD8T+ajpLwE3Vp0w3uj3kNg/UBqGkhOTtb6TN3KMs5gt9vxVMpTuDPkTsQPjK/ydbomDlYxhpkSamsK2fTtJvxp359ggw0N6jXAihEr0Cesj/a4HD58WCvGPq+2FUsSvknA24fehg02RAZH4p2R7yCkYQgWL16svDYvL0+Jbdu2TYnptilzluSTyXgi6QkUzi8EwM1fLMbMELotsWrLc7uew/vH30ewfzAAoGOzjtgas5WaU3RNdiZzl3H00lHMODQDBaUF8LZ5Y9mwZejRogfatm2rvNaZFpTsfNTG0LTxyEYs/Xpp5d/5JfnIKshC1pwstAhoQd/DDDTMGLds2TIlZqpCU9KJJCz8bCG8bF4I9g/GOyPfQWRwZJXfwUxmbAxNz8e3svLASiQcTIC/jz86N++MVVGrKnOb6WM5zdYaltMst243sfr4+CA8PFxP/P/H6TvXn6/+jAkpE/C3R/+G72Z8h/ZN2mPe39XydJ7GiZ9P4N6N92L78e1WS9Hiu5zvMPfjudg5bifSp6Tjv+7+L4zZOsZqWQ5Jy07Dkn1LsG/iPhybdgwdgjtgwacLrJalxenLpxG/O94yR29N2Ze1D1titiB9SjrSp6Rja8xWqyU5pKisCMM3Dces3rPw+e8+x9x+czF5l+t78jrD+O7jK8f34DMHERoQioQRCVUurJ5AcVkxYpNi8cFjHyB9SjpG/nokZu2cZbUsh+w5uweLv1qMT8Z/gvQp6Yj6VRQmf+jZucFwenHdfWY3+rbqiw5NOwAApvadis1HN3v8pLTq4CpM6jUJj3R5xGopWtT3qY93R76Llo1aAgD6hPXBxSsXUVpearGyqukd1hunZ55GY7/GuHb9Gs4XnkfTBk2tllUtRWVFiE2KxdIHllb/Yg+g5HoJDl84jDe+egN3vn0nHt72MM7ln7NalkN2n9mNyKBIDI8YDgAY0X4E1o1YZ7EqfRZ/tRghDUMQ10f9qZgnUW4vh91uR/61fADAldIr8PPxs1iVY9IupOG+9vchPPDGneKYzmPw4akPPXquYzj9WPjHgh/ROrB15d/hgeEoKClAYWmhsx/tUhKiEgDcuMjrAu2atEO7Ju0A3Hic/eyuZzGq4yj4evtaK6wa6nnXQ/LJZEz670mo71Mfi4YuslpStcR9FIe43nHo1qKb1VK0yC7MxrCIYfjDsD/gjuZ3YMm+JYjeEo1/TP6H1dKq5NTlUwgNCMXMj2fiWM4xNK7fGK8OMlv43lXkFOXgza/fRNrkNKulVEuAbwBWP7gaA9cNRFP/pii3l+OriV9ZLcsh/Vv1x4oDK5D5SybaNmmLxPRElJaX4nLR5cqbi7qA03euFfYK2GBT4t42b2c/WiBcLb2KR7c/iozcDLw76l2r5WgxutNo5Dyfg1eGvIIHNj2ACnuF1ZKq5K2Db8HHywcTe060Woo2EUERSB2Xiq4hXWGz2RA/MB5ncs/gh19+sFpalZSVlyH1dCqevPNJ7Hl8DyZ3n4xHUx5FyfUSq6VVy9q0tYjuGI32QWoXJ0/j6KWjWLR3EY5PO47s57Lx0uCX8PC2hz36yeLgtoOxcMhCPLT1IfRZ26dyr9jTbyRux+k71zaN2+DA+QOVf58vOI8gvyA09G1IN/N1N6PZBrpV7dFYtSNWjYkdm0lTyLn8cxj515Ho3Kwz9jy5B/71/AFw0wTbzNc1sui818vLC4GBgQ4/JyM3AxevXMSgNoMAABN7TsSUHVOQV5yH119/XXk9Myrdd999SmzNmjUOv9cZ1qevR1FZEXqs7oHS8lIUXy9Gj9U9kDouFWGNwvQ+g+TuwoULDSv9X7699C2OXDyCJ7o/URmzw4563vVo9TOWp6zlnG4bydoQ1igMnZt3xn2dbpzf3/X+HWZ/Ohu59lyaf7pzCZsjTBtvtv5zK1b8ZoXWa5lG1hrQVRWjdp3Zhbva3FVpYJredzrm7JqDy8WX0axBM/q9rCIdi7lKc2FJIYa0G4Knez0N4MaasmDPgkpDk655icHmXmbWNGHcc/rOdXjkcOzP2o/Tl08DAFYfWo3oTp5dUq8uUlhSiKHrh2JMpzHYErOlcmH1ZC4UXsDY7WORU5QDANh8dDO6hnT16H3Xb575BsemHUP6lHSkjkuFv48/0qekay+sVuBl88KsnbNwNu9GucK3D72Nbi26Ve5ZeSIjOozA2byzSMu+8Wj188zPYYMNEUFqGUZPIq84Dxm5GRjYeqDVUrTo1bIX9v6wF5euXAJwwwEf0SQCzRo0s1hZ1WQXZmPo+qEoKCkAALz2xWt4vOvjsNnUJ6SejNN3riENQ5AYnYiY92NQWl6KyKBIbHxoowltwi0kfJOAzPxMJJ1MQtLJ/61n+8n4TyxU5ZjBbQfjpcEvYej6ofDx8kFYozAkP6b3L0xBn64hXbFyxEqM/OtIlNvLER4Yjr8+/FerZTkkNCAUyWOTMS11Gq6WXkV9n/r44LEPPN5sk5GbgZYBLVHPu57VUrQYFjEMcwfOxdANQ+Hr7Ytg/2CkjFV/CuRJdGzWEfMGzUP/d/ujwl6BQa0HVXpk6hJGfuca1SEKUR2iTHyU21k/er3VErSYP3g+5g+eb7WMGjO171RM7TvVahm1ol2Tdrjy4hWrZWgR2y0Wsd1irZZRI+5uezcOTDpQ/Qs9iL6t+iJjVobVMmrE9H7TMb3fdKtl1IgZ/WZgRr8ZVstwCiOLa5Uf7qN+fGhoqBJr0KCBEvPz85x/weoeR5s2bZSYO46D7X0yfbqw8+Hl9a87CM4+omndurUSa9y4sRJr0UL9DSE7H54EG3ur/AIhISFKjP0YPjg42B1ytPD1VY0rutcbuxaq8wa4Et357vbry12wvGT5wcbQKs2m5ztX5YdLW84JgiAIwv9FpLawIAiCIBhGFldBEARBMIwsroIgCIJgGFlcBUEQBMEwLrVdxserbdz279+vxLp06aLEWEUW9jrTZGVlKbHJk9WODMOHD1diTLNpdPUdP3681t/Bjm3t2rW1/rzt29XOQ+vWqUXa2fjpHsfEiWq5Qne4RHfvVmtTL1qk1k/esmWLEqtpC6vqYGM1duxYJcbGisH0xcTE1FxYDWH5wipNDRgwQImxHDI9zlURFaX+HJHNWS+//LISs8rRzK5DBjsnLN9YnrPzpEtBQYESY7nAjoONPctf3euhprh0cc3JyVFibHFo1kytFlJaak0HhOvXryux7OxsJeaunqK3o6vPmf6XP/30U63fy7hyRf2tKNNcXFysxHTHuaLCmnrFTDPLcXbeTMOumXPn1O44umNq1c+HWL6wMWXzizvGuSpYTrOfQlmVqwy2eDF055hr1645relW2Fjl5uZqaWH5q3u8JpDHwoIgCIJgGGN3rqyY8oYNG5QYK1rNCmuzmG6hbmdgx3HkyBGtGCv2bLq4ta6WJ598Uok99NBDSowVbmCFrJ2BNS9gmp0psO6OsWd3fKwgv6sKgVcH05efn6/EXn1Vr7Ubu1ZZMX/Tx6a7vcLmAzb27Jp2NsdTUtQSgiyn2TlhjR3csaXEYFoYTB97LzsnzjSAYN/BivTv2bOn1u911djLnasgCIIgGEYWV0EQBEEwjCyugiAIgmAYWVwFQRAEwTBuby/CNpSZIYK9zh1GgKCgICXGTD+6mk2bavLy8rRexwwbbdu21XqdaXQNL7Nnz1ZiuuYgZ0wTuuzdu1eJMRMLywN3oGvuY+PMriN3mLCY2Y2ZsJhBj80HLF/Y69hvJWsCmxMY7Ppn322VoYmNF9PMxpDlm+n5jn0HM4mxa44ZaqOjo80I00DuXAVBEATBMLK4CoIgCIJhZHEVBEEQBMPI4ioIgiAIhnFphSaGrkmCbWRHRETUQFHtYBver7zyihKbM2eOEmPmDNOwCigMpo+RmJioxEybEnRZvny5EmPGEWZUcAfMTMb06VbvMo2uoYmNMzOsuKKy0e3oambVxXQ/75577qmRJh3Y+WSGQd1qWGz83XEdsuNg48UMZe4w7rExYHMgm3uXLVumxJw1stUEuXMVBEEQBMPI4ioIgiAIhpHFVRAEQRAMI4urIAiCIBjGZrfb7SY+iJl+2Ga+7texjXa26e+Oyjy66FZtcsYUwjbz2XewcWGb+cy04o5G8Ox7dSvz6LYVcwY2Buz8Ms3M5MTOh27VG2dg55x9h261I3cYQmw2mxI7fPiwEmP6WIxVP3KFWYjljO71yvKXxZzJD6aFtXlkjccNLROWw8aPjYsJA6LcuQqCIAiCYWRxFQRBEATDyOIqCIIgCIaRxVUQBEEQDOP2lnMMZgRgm+ruaI/mDMw4wYxezlQ20TVxsDF1h1FJF2bwYcYCZhhyRx7oGpp0TSzMOMJyw7RhiOUk08y0uKOqFNPCDGFsPtCtVsbG3hUwswzLcxZjOW26haXu57FxTUlJUWLubN9mCjb2zDhmwvAmd66CIAiCYBhZXAVBEATBMLK4CoIgCIJhZHEVBEEQBMMYMzSxjWJWoYkZGNjmMTM1mK5eowvb4GfHwUwJOq3pfHx8EB4eXmt9bEOeGVlYKzTWcs40bKyYcYcZLlgeWNWKi2lm7blYtSOrzGTMiDJ79mwlpmtyMg27ppmJjeUpuy7ZPOSueYONNass1bNnTyXGdLPxdyb3dauEsdxnx+EOQxM7x2wMmGmNjTP7vAkTJtROXDUYWVx3nNqBWYdmoayiDO0btsfcjnPR0KehiY92GSsPrETCwQT4+/ijc/POWBW1CsH+wVbLcshzu57D+8ffr9TZsVlHbI3ZarEqxzy36zlsPbYVQX5BAIAOQR2wLmqdxaocc/TSUcz8n5nIL8mHt80bax5cg95hva2W5ZC6mBs7Tu3A/E/mo6S8BN1adMN7o95DYP1Aq2U5pC7OG0knkrDws4UoulqEwHqBeO7Xz6GVfyurZTnk5jijDIhoFIH53eajsa/6D21PxunF9eerP2NCygQsvWMpwhuEY833a7D27FrM6aDXrNsK9pzdg8VfLcb+SfsRHhiOvxz5CyZ/OBnbH91utTSH7Mvahy0xWzCw9UCrpWizL2sf3hvxHvqH9bdaihZFZUUYvmk43hv1HqI6RCHlZArGfTAOJ2ectFqaQ+pabtycN76a+BU6NO2AFz5+AfP+Pg9v/fYtq6VVSV2cN4rLihGbFIsjU44g69ssvJ/1PlZmrMTrd75utbQquXWcc77PwUc/foTfp/8eS/otsVpajXB6z3X3md3o26ovwhvceKwZHRaNTy594tGFntMupOG+9vchPPCG5jGdx+DDUx+itLzUYmVVU3K9BIcvHMYbX72BO9++Ew9vexjn8s9ZLcshNzWvSFuBgZsGYvxH4/FjwY9Wy3LI7jO7ERkUiagOUQCAUR1HYdsj2yxW5Zi6mBs3540OTTsAAKb2nYrNRzfLvGGYcns57HY78q/deNReXF4MXy9fi1U55vZxvrflvfj80ucoqyizWFnNcHpx/bHgR7QObF35d/P6zXG1/CqKyouc/WiX0b9Vf3x69lNk/nLjh+mJ6YkoLS/F5aLLFiurmuzCbAyLGIY/DPsDvp3yLQa0GoDoLdEePRnd1PzSf7yEr8Z9hT4t+2DcR+M8WvOpy6cQGhCKp1OeRp+1fXD/X+7H9YrrVstySF3MjdvnjfDAcBSUFKCwtNBCVY6pi/NGgG8AVj+4GgPXDUTM1zFIPp+MuPZxVstyyO3jnHIuBWUVZfil1HOK4Ojg9GPhCnsFbLBVbpZfr7gOfA4MvXso3fAOCgpSYkOGDFFipluI3crgtoOxcMhCPLT1IXjZvDCx50QE+wfD19tXMRsB3AigW62ne/fuBhQDEUERSB2Xih9++AGZmZmICYvBor2L8OWxL6mphhmBFi5cqMRcaQ66qfmzzz7DkawjGOw1GItzF2Pn/p3U7MY0szxwZYWmsvIypJ5OxZ4n96B/eH+knExB1OYoZP5nJs0DZsJi5gpXGrNujvMvv/yC/Px8TOoyCb///Pf49ty3tGUfM/iwvHelEejmvHE73jZvLFu2TIkzYyCbX1zZEs/RvAHw64vBNDITl4m54+ilo1i0dxGOTzuOpl5NsSZ9Df74zz/ii999AZvNRuesDRs2KDF3GB9vQsc5Ixi9uvVC0wZNtY1Zuq042fpjAqfvXNs0boPsK9mVf58vOI8gvyA09PVcQ1NhSSGGtBuCf8T9A4cmH0J0xxsXqScbE7699C3+cuQv/xKz2+3w8fKICpYUqhmerTmsURg6N++M/uE39oijO0Wj3F6O7/O+t1hZ1dTF3JB5wz3sOrMLd7W5C5HBkQCASd0m4cTlE8i9lmuxsqqpi+PMcHpxHR45HPuz9uP05dMAgNWHViO6k2fXnMwuzMbQ9UNRUFIAAHjti9fweNfHaZNmT8HL5oVZO2fhx8Ibe5abvtuETkGd0LJhS4uVVc1NzReKLwAAUrJT0L5hezSv39xiZVUzosMInM07i7TsNADA55mfwwYbIoIiLFZWNTfHOTP/xmO09759D12adUGrRp7rCJV5wz30atkLe3/Yi0tXLgEAdpzZgbaBbdHUv6nFyqqmLo4zw+l/2oY0DEFidCJi3o9BaXkpIoMisfGhjSa0uYyOzTpi3qB56P9uf1TYKzCo9SAkRCVYLcshXUO6YuWIlZj06SSU28sR2iAUy+9ebrUsh9zU/OKuF1GBCjT3bY4FnRdYLcshoQGhSB6bjGmp03C19Crq+9THB499AD8fP6ulVcnNcR774VhUVFQgLCAM7/7mXatlOUTmDfcwLGIY5g6ci6EbhsIb3gjyC8LmkZutluWQujjODCPPjaI6RFW6K28lJCREibVt21aJhYaGKjFfX9c62mb0m4EZ/WYocR8fdUjatGmjxMLCwpRYQECAEgsMVH+3d/t3eHt7O9R6k9husRgaPFSJszFl32tFEY7YbrH4VdGvlLiuZlfnAePutnfjwKQDWq/19/dXYlYcW2y3WIxqN0qJs9xlWry83F+srap5g+ljY8rmF1cfR1XzBsDnBEZFRYUSKygoqPXnVcf0ftMxvd90+h26czSb21yJo3EODlYfD7MiPCyPmjVrpsRclTM2uydbCgVBEAShDiK1hQVBEATBMLK4CoIgCIJhZHEVBEEQBMPI4ioIgiAIhnHpr8wnT56sxJiDKysrS4mxlmkDBgwwI6yGLFq0SIlt364W6/7yyy+VGDteqxg7dqwSmzhxohIbPny4O+Qo6I5zly5dlNiWLVtcoulWmL5162rf4Wft2rVKzPTY62pmY8pyIyYmxoyw/w9zsA4aNEiJsbGyaj6oCez44uPjlRhzu7788ssu0XQrbO6NilId3EzfkiVqIX2WR6bRzRmmmc0TrpqjXbq4/vTTT0qstFQtcn3x4kUldu3aNZdoqg25uWo1k3Pn1MLozGLvSbBxLi4utkAJR3ecmRXfHTB9mZmZtf48d4y9rmb2M60rV664RNOtsGuGnXNPmg9qAjs+Ni82aNDAHXIUrl9X62az8Wc/V2FzuTvQzRmm2Z1ztDwWFgRBEATDyOIqCIIgCIZx6WNh1nFBt+sM6/SSl5enxExXHWJdWJYvV8sMsk4KVlRAqgo2znv37tV6L+s2Yho2zqzT0CuvvKLEXNn55CYsd1mXDdZ1hh0H6wJ0+PBhJWZ67FkHId1OPhMmTFBiprsosXFmHWLYfMBg1YV0c80VsG4tKSkpSsxU96yawq4vBhsvNm+z82kaNqYsZ5gWNnfojkFNkTtXQRAEQTCMLK6CIAiCYBhZXAVBEATBMLK4CoIgCIJhXGpoYiYJthnNzDcMdxiGmGZmkmDHwd7LNsuZycQZ2Ma9rvHEKhMWM0OwGIONfXp6uhJzZpzZuOjmqa7hiuWLaVgesJxs3LixEtuwYYMLFP0rzGzEYEYv3fPrDpMNwI9FNxd0c98Z2DXCznFiYqISY3lk2tzGYLnKxnn27NlKjBUiYtccOw4Thje5cxUEQRAEw8jiKgiCIAiGkcVVEARBEAwji6sgCIIgGMalhia2UTxnzhwlxgwHe/bscYWkf4FtjLNKH+w4mLmFVV5hxhhmyNGFfS/Tp1uNyR2GJjbOrEqQMyYi0xV3mPmDaWav0zXpMLMG+w7T9OzZU4mxsWdGPtMEBQXV+r3sONxRXQzg1xwzBzE9uo0TTKNr7NK95phhiOW0MxWQ2LgwIxv7DvZeppldrybMWnLnKgiCIAiGkcVVEARBEAwji6sgCIIgGEYWV0EQBEEwjEsNTWzzmME2nt1RsUTXPMI2+HWPzXQ7I7b5zsZPtz2aO8aZwdr4sSpBzGDGYMfrjCmBfR5rG8dgx8GMLa5qdVUdTAszELLcYAYuZ8xkTAuLse9lVXnc1QqSmRJ1K4cx46M7WuCxuYOZ1nQrh7mj8lVERIQSY/Ox7jlmZihnDKaOkDtXQRAEQTCMLK6CIAiCYBhZXAVBEATBMLK4CoIgCIJhbHa73e6qD2eb9MwIxMwjbFNd10TkDOw7mPmGwcwButVOTMPMBqwaDjOF6LbJcgcsh1i+mG7jx2Dnkhkuli1bpsTckbumYdcgyyvdilTOwExATJ+njT0zNLHKUgsXLlRipg1vTAszrekahnSrk5kef10tbO7QbWFnIqflzlUQBEEQDCOLqyAIgiAYRhZXQRAEQTCMLK6CIAiCYBhjFZp0N4CZ8YQZVFjVDHcYE5iZh22gs2o9VlXcYZhuL+UO2PllpgR3mJcYupVcTLSrMoWuWUPXnOKOsWfjPGHCBK33WpUbVcHayzHccR2ysWFzG8tfZh5j852u+dMZmD52bGzuOHLkiBJLTEw0IUvByOKadCIJ8YfiYYMNgfUC8dyvn0Mr/1YmPtqlJJ9MxhNJT6BwfqHVUrSx2+14KuUp3BlyJ+IHxlstxyEbj2zE0q+XVv6dX5KPrIIsZM3JQouAFhYqqxpHmj2ZhG8S8Paht2GDDZHBkXhn5DsIaRhitSyHPLfrObx//H0E+wcDADo264itMVstVlU9dekaBIAdp3Zg/ifzUVJegm4tuuG9Ue8hsH6g1bIcUhc1347Tj4WLy4oRmxSLRXcswrt93sV/NP0PrMxYaUKbSzl9+TTid8fDhb9EMs6Jn0/g3o33Yvvx7VZL0WJ89/FIn5KO9CnpOPjMQYQGhCJhRILHLqxA3dSclp2GJfuWYN/EfTg27Rg6BHfAgk8XWC2rWvZl7cOWmC2V410XFta6dg3+fPVnTEiZgL89+jd8N+M7tG/SHvP+Ps9qWQ6pi5oZTi+u5fZy2O12XLl+BQBQXF4MXy9fp4W5kqKyIsQmxWLpA0urf7EHsergKkzqNQmPdHnEaik1ZvFXixHSMARxfeKslqJNXdHcO6w3Ts88jcZ+jXHt+jWcLzyPpg2aWi3LISXXS3D4wmG88dUbuPPtO/HwtodxLv+c1bKqpa5dg7vP7EbfVn3RoWkHAMDUvlOx+ehmj76pqIuaGU4/Fg7wDcDqB1djUsokBNYLRIW9Ait7evada9xHcYjrHYduLbpZLaVGJEQlALiRfHWJnKIcvPn1m0ibnGa1FG3qmuZ63vWQfDIZk/57Eur71MeioYusluSQ7MJsDIsYhj8M+wPuaH4Hluxbgugt0fjH5H9YLc0hde0a/LHgR7QObF35d3hgOApKClBYWuixj1nromaG04vr0UtHsWjvIhwYfwARTSKwJn0N/vjPP+KL331BN5nZZjTbeHZVlaC3Dr4FHy8fTOw5ET/88oPWe3TNQVa1b2PoanaHAWRt2lpEd4xG+6D2Dl/HTB267a9MwzTrtvFzRYszHUZ3Go3RnUbjnbR38MCmB5AxK4NeR7rVbJjBzJRpLyIoAqnjUiv/jh8Yj99//nv88MsP9DtY9TNmfPKkaxAAunfvrsTYsbhKd4W9AjbYlLi3zRsAH0M2/iwXWG6xdoE1pTrN7HuZ+Y7NgawSlqsMiE4/Ft51ZhfuanMXIprcKAM3qdsknLh8ArnXcp0W5wrWp6/HwfMH0WN1D0RtjkLx9WL0WN0D2YXZVkv7t2XrP7diQg89t6enUJc0Z+Rm4MtzX1b+PbHnRGTmZyKvOM9CVY759tK3+MuRv/xLzA476nnXs0jRvydtGrdB9pX/ndvOF5xHkF8QGvo2tFCVY+qiZobTi2uvlr2w94e9+OnqTwCAHWd2oG1gWzT198w9n2+e+QbHph1D+pR0pI5Lhb+PP9KnpCOsUZjV0v4tySvOQ0ZuBga2Hmi1FG3qmuYLhRcwdvtY5BTlAAA2H92MriFdPXrf1cvmhVk7Z+Fs3lkAwNuH3ka3Ft0QHhhusbJ/L4ZHDsf+rP04ffk0AGD1odWI7uT83aUrqYuaGU4/Fh4WMQxzB87FyL+NRD3vegjyC8LmkZtNaBP+DcjIzUDLgJZ16o6krmke3HYwXhr8EoauHwofLx+ENQpD8mPqI2xPomtIV6wcsRIj/zoS5fZyhAeG468P/9VqWf92hDQMQWJ0ImLej0FpeSkigyKx8aGNVstySF3UzDDyO9fp/abjiU5PKPGQEPV3duHh6r9MQ0NDlZifn58JaQ5p16Qdrrx4pdrXBQcHKzG2b+LjY6wmh0PWj15f7Wt8fVXHNtPs6v3Bvq36ImNWhtZrWb4EBrrfwFCV5rAw9elGs2bN3CGpWqb2nYqpfacqcWeuQVfnRmy3WMR2i1XiTB/DHXNEVehcgwCfE9jxBQQEOCupSqI6RCGqQxT9f2wMWS60adNGiVmlmcGuTabPnX4Il7acEwRBEIT/i0htYUEQBEEwjCyugiAIgmAYWVwFQRAEwTCyuAqCIAiCYdxjb72F3bvVsslB3l8AACAASURBVGGLFqml2pijbsmSJVqvM83x48eV2NixY5XY8OHDldjLL7+sxEw7YJk+Nqb79+/X+jw2zjExMTUX5gCmhcVYNZa1a9cqMTb27qCgoECJRUXpuRzZcXTp0sVpTbeSlaV282G5y46D5YFV48zQPY4tW7YoMXe50JkeltPsPLH8cAfx8WqnHzbHuGNc2fixuY2tKwMGDFBiLKddlQtuX1yLi4uVGEssZmG/fv26SzRVR2lpqRI7d04tMp6Tk6PEKioqXKLpVpi+ixcvKjHd3pJXrlT/8yRnuXbtmhJj5cqYZpZDVsHOL8tnBjtvpmHXDNPHxt6TxpnBcpwdhzuuwapg352bq1av++mnn9whRws2j2VnqxXs3DGu7DuYPjYft2+vllt1Zy7IY2FBEARBMIwsroIgCIJgGJcWkWB7C7odF1i3Bqu6YLDOLKzSx4YNG5TYnj17lJhpzayrA3s8xr53zpw5Sox1tmAdYZyBnfPly5crMdbFgnV1Ma1PF92x1+34Y6rrzE2YPpanjMaNGysxdhzuqHrDxuXVV19VYkyzbhcgV8C6Th05ckTrvaanZpaXuvnLcpVdw6Zx5lpn72XHxjrqmEDuXAVBEATBMLK4CoIgCIJhZHEVBEEQBMPI4ioIgiAIhnHp71yZ0UF3k5ltWjNzgDtg+pi5imlmBhDTMC0MpoWZztxhULnnnnuUGBtnZmRhecCOzR2mFaaFGT3ckbvs2mLmpSeffFLr89h72Xe4w6Cna8Ji4+wu8xKDXUvLli1TYuw6NA27vlJSUpTYkCFDlJg7zEsMdj5ZjB0HmxfZGLCYiTlQ7lwFQRAEwTCyuAqCIAiCYWRxFQRBEATDyOIqCIIgCIZxqaHJmeofVhltGEwLM3YwTJsp2MZ9YmKiEmOb/rqF8d1hXmBVoFg1JlY9xR0mMV3YWLF8YZpNj7NuYwZdA5w7xp59h655icFy3ErY8bE5wR3XnO65s2qedQY2B7JcYPO2q45X7lwFQRAEwTCyuAqCIAiCYWRxFQRBEATDyOIqCIIgCIZxqaFJt8URq/DijlZyurBNcF0ji+njYC3smMmJxXRhhhd3VJBhY8UqOTHjk2njGDOi6MZ022RZZRxh55ddl2xM3WEYYhWMmEGP5YZVlYQAfs2xY7HK0KQLy2lWKc2T5m02fq5qJaeL3LkKgiAIgmFkcRUEQRAEw8jiKgiCIAiGkcVVEARBEAxjzNDENrxfffVVJda9e3clxswe7oBteLNqUfn5+Ups9uzZSoyZQkzDxplpZmO6fPlyJcYqm1h1HMxo07ZtWyXWs2dPFyj6V1g1G5bPDDam7jB6sFZhjRs3VmLMnKZrXjJtwtI1xegaqdxVvYuN4Zw5c7Tey/LDk2DzHZtP2PVg1XzCcoaZ4HTnHRPInasgCIIgGEYWV0EQBEEwjCyugiAIgmAYWVwFQRAEwTA2u91uN/FBrDoJM/2wtlis/Ziu6cI0bBP8yJEjSowZRdjGPYux7zANMzmxykFWtW+z2WxKjJkmWA4xA4I7DENsrHTNGsxIwfLZdI6zPNCt3sVy3KqWbux7g4KClBir3sXG3lmYGZLlL3sdm0/YHMiqDpnOc11jFqs0xa5Ddp5MzzFsHmPfy/SxdoZ5eXlKzIRxz4hbeNO3m7Dg5AIAQH2v+ngm/Bn8qsGvTHy0y9j07Sb8ad+fYIMNDeo1wIoRK9AnrI/Vsqpk45GNWPr10sq/80vykVWQhaw5WWgR0MJCZY5JOpGEhZ8thJfNC8H+wXhn5DuIDI60WpZD6lpuAMCOUzsw/5P5KCkvQbcW3fDeqPcQWD/QalnVYrfb8VTKU7gz5E7ED4y3Wk613MyNq1euws/bDzN/NRMdG3W0WpZDjl46ipn/MxP5JfnwtnljzYNr0Dust9WyHJLwTQLePvQ2bLAhMjgS74x8ByENQ6yWVSOcfiz8Xc53mPvxXLwc+TL+3OnPeCT0Ebx+9nUT2lzGTc07x+1E+pR0/Nfd/4UxW8dYLcsh47uPR/qUdKRPScfBZw4iNCAUCSMSPHphLS4rRmxSLD547AOkT0nHyF+PxKyds6yW5ZC6mBs/X/0ZE1Im4G+P/g3fzfgO7Zu0x7y/z7NaVrWc+PkE7t14L7Yf3261FC1uzY13+7yL2DaxePmfL1styyFFZUUYvmk4nr/reRyOO4wFdy/AuA/GWS3LIWnZaViybwn2TdyHY9OOoUNwByz4dIHVsmqM04trfZ/6eHfkuwiuFwwA+JX/r/DL9V9QVlHmtDhXcVNzy0YtAQB9wvrg4pWLKC0vtViZHou/WoyQhiGI6xNntRSHlNvLYbfbkX/txu/mrpRegZ+Pn8WqHFMXc2P3md3o26ovOjTtAACY2ncqNh/dDEM7Pi5j1cFVmNRrEh7p8ojVUrS4PTc6NuqI3NJcj57rdp/ZjcigSER1iAIAjOo4Ctse2WaxKsf0DuuN0zNPo7FfY1y7fg3nC8+jaYOmVsuqMU4/Fm7XpB3aNWmHlBMpsNvtSMxORN/AvqjnVc+EPpdwUzNw47HUs7uexaiOo+Dr7WutMA1yinLw5tdvIm1ymtVSqiXANwCrH1yNgesGoql/U5Tby/HVxK+sluWQupgbPxb8iNaBrSv/Dg8MR0FJAQpLCy1UVT0JUQkAbiwAdYHbc+OtM29hYNOBHj3Xnbp8CqEBoXg65WkcuXQETfya4I3737BaVrXU866H5JPJmPTfk1Dfpz4WDV1ktaQaY6xC030j7sNTKU/hWoNr2Bm7E038mtBNerbJzGLMiMFe54w56GrpVTyV8hR+zP8RO2N3AuCb5brmBRZjBhVnNK9NW4vojtFoH9TeoT5mWnF3Jayjl45i0d5FOD7tOCKDI7HiwAo8vO1hpMel00ou7JyzqkPuMISx3GDnko2pbus83Xypjgp7BWxQDWLeNm/tVoHMdOKONoO6MIMJyw13tBm7WnoVb+W8hcJ6hZVzHaBvNtJtj2bCVFNWXobU06nY8+Qe9A/vj5STKYjaHIXM/8xEfZ/62q3udE1hzDBUW0Z3Go3RnUbjnbR38MCmB5AxKwNeNi9qItQ1nT755JNKzFWtH438FOdc/jkMXDcQ3jZv7HlyT2WyeTJ1UTMAbP3nVkzoMcFqGVrsOrMLd7W5q9LANL3vdBz76RguF1+2WJlj6lputGncBtlXsiv/Pl9wHkF+QWjo29BCVf+e1LXcCGsUhs7NO6N/eH8AQHSnaJTby/F93vcWK6uajNwMfHnuy8q/J/aciMz8TOQVq65eT8bpxbWwpBBD1w/FmE5jsCVmC/zr+ZvQ5VLqomYAyCvOQ0ZuBga2Hmi1FC16teyFvT/sxaUrlwAAySeTEdEkAs0aNLNYWdXUxdwYHjkc+7P24/Tl0wCA1YdWI7qT+tRIcI66mBsjOozA2byzSMu+sY30eebnsMGGiKAIi5VVzYXCCxi7fSxyinIAAJuPbkbXkK51bt/V6cfCCd8kIDM/E0knk5B0Mqky/sn4Tzx2MOqiZuDGv+haBrREPW/P3eO5lWERwzB34FwM3TAUvt6+CPYPRspYvd9YWkVdzI2QhiFIjE5EzPsxKC0vRWRQJDY+tNFqWf921MXcCA0IRfLYZExLnYarpVdR36c+PnjsA482Fg5uOxgvDX4JQ9cPhY+XD8IahSH5MWuauziD04vr/MHzMX/wfO3XBwQEKLHQ0FAl1qBBAyXm62vGVFJTzX5+aiIGBwcrsfDwcCUWGGjut4Z9W/VFxqwMJc70sW4y/v7u/5f29H7TMb3fdCXO8oBpZrnh5eW6wmI1zQ2mJSwsTImxH7mbPB9RHaIqHaG34uOjXuIhIervBUtLVTc0yytXsH70+lq9j+WGK6lpblQFmztYzpia7+5uezcOTDqg/Xo2Z7Vp00brvaZyemrfqZjadyr9f7rXFzuOZs3c99TMWIUmQRAEQRBuILWFBUEQBMEwsrgKgiAIgmFkcRUEQRAEw8jiKgiCIAiGMVahiREfr3a52L1bLXXGXF1LlixRYgMGDDAjzAHbt6tFxNetW6fEjh8/rsSY5piYGDPCHDBo0CAllpWVpcSYm5lVaDGtmY1VVJTqbGUwzRMnTlRiTLNJp3ZVsHxheb9lyxYl5o58ZixapJaSKygoUGIsn62CjSnTvHbtWnfIobC5jY31l19+qcQ8CaaZ8fLL1jQtYNcci7H80J07TODSxTUnJ0eJnTt3Tomx8lPXrl1ziabquHLlihLLzs5WYqwvLXuvO2ALKdPHcIdm9jMPXX0MdtFUVFTU+vOcgY0fOzar8pmRm5urxKzq06oLm0s8TXNxcbESY9emp8Pyw5Ng19zFixeVGMsPd87R8lhYEARBEAwji6sgCIIgGMalRSRYpwfd7husy8HZs2eVWG26iNyEdcDp2bOnEmOVg9gjh/z8fCWWl6cWmzbdhYGNHzs23Y4Vhw8fVmLOdKJhY6XbZYN1QmLjvGfPHiWm26VEF3Yc7DtYjrOYq7px3ArLjTlz5iixZcuWKTHdjimmYd+7fPlyJcY6K7GOKe6C5QK7bjyp25DuHDh79mwl5o7jYNf/hAlq4xKmjx3b3r17lZjpdeUmcucqCIIgCIaRxVUQBEEQDCOLqyAIgiAYRhZXQRAEQTCMS3/nyjb42SYzMy+xDWoTm8y3wswGyclq38DoaLXxNDPkvPrqq0qMmWBMG1mYASQlRe2bqmtoMj3O7HiZGYLFmHlpyJAhSswZwxWDnTdmlmG5y3LIHeYlBjOEdO/eXYlZaQS6nbqoGeBzm6ebl0aPHq3EnnzySSXGjoOZ9EzPHc78lpkdL8sj05pvIneugiAIgmAYWVwFQRAEwTCyuAqCIAiCYWRxFQRBEATDuNTQxNDdPDZtUNGFmZecwari4mzjnrFw4UIlZpX5hhkkGMzw4o6qV8wkxswfLIeY+YYZ/kybdNiYMjOeVeecwbRYNR9UhW6FNk/SzXKaNZl46KGHtN7L8sh0JTJm1mSV/3QrubnTYCZ3roIgCIJgGFlcBUEQBMEwsrgKgiAIgmFkcRUEQRAEw7i05ZwuzMTBNsbZRrYzsAoezGTCNsZ1sapVE4MdGzNmsHF2h+FF93wwI4Xp9mjse1m7KmYIY2PKWqaxSlPO5Dh77z333KPEGjdurMSY8YZV7zE9zmysmOmR6dM1hLmiAg/THRQUpMRYOzNWwUv3+JyBzam6hiE2BzLTpFVzBxsrdt6Z8clVyJ2rIAiCIBhGFldBEARBMIwsroIgCIJgGFlcBUEQBMEwHmFo0jUH7NmzR4k5s+nPKu7oGlRYZRPd1nRWVW3RNbwsW7ZMiZk2sujCvpeZEkxXwmLmD3benDG7mR5npjkiIkKJMSMVy1NmvGPmFNMmEV0zmS46LdS8vLwQGBio/ZnMfNezZ8+ai3OAVWZI9h1z5sxRYocPH1Zi7pjbdMfe9HpRU+TOVRAEQRAMI4urIAiCIBhGFldBEARBMIwsroIgCIJgGCMt5xK+ScCqA6sAGxDROALL712O5g2aU5MEM9Dk5eVpfY9uBZ/q2HhkI5Z+vbTy7/ySfGQVZCFrThbSo/W+gxloXN0KLeGbBLx96G3YYENkcCTeGfkOQhqGUGMW28xnlWFcTVW5oWvc2bBhg9brWG44Y65o164dkk8m44mkJ1A4vxCAfjUrluPM7GbSJHb00lHM/Gwm8kvy4W3zxpoH16B3WG9q0NNtz8Uq3JjMoaOXjmLm/6iaWWUoZmhixiwGy6HbKzn5+flhwIABtdYM8MpXDDaG7Jywql7OGJpuz+eqYHOWblUvU6w8sBIJBxPg7+OPzs07Y1XUKgT7BwPQr7jlispcNcHpO9e07DQs2bcEOx/dia9jv0Zkk0i89vVrJrS5jPHdxyN9SjrSp6Tj4DMHERoQioQRCWgR0MJqaVVyc5z3TdyHY9OOoUNwByz4dIHVshxSF3PjJqcvn0b87nh4gJm+WorKijB803A8f9fzOBx3GAvuXoBxH4yzWpZDRLN7qUv5vOfsHiz+ajE+Gf8J0qekI+pXUZj84WSrZdUYpxfX3mG9cXrmaTSu3xjXrl9D9pVsBPsFm9DmFhZ/tRghDUMQ1yfOaikOqRxnvxvjfL7wPJo2aGq1LIfU1dwoKitCbFIslj6wtPoXewC7z+xGZFAkojpEAQBGdRyFbY9ss1iVY0Sz+6hr+Zx2IQ33tb8P4YHhAIAxncfgw1MforS81GJlNcPInms973rYcWYH7njvDnx9/muM61I3/jWXU5SDN79+E8seUH9v6InU866H5JPJCF8ajs8zP8eEHhOsllQtdTE34j6KQ1zvOHRr0c1qKVqcunwKoQGheDrlafRZ2wf3/+V+XK+4brUsh4hm91HX8rl/q/749OynyPzlRi2BxPRElJaX4nLRZYuV1QxjhqbfRv4WZ+LO4IUBL+Dh5IdRYa8w9dEuY23aWkR3jEb7oPZWS9FmdKfRyHk+B68MeQUPbHqgToxzXcqNtw6+BR8vH0zsOdFqKdqUlZch9XQqJveejEOTD2Fmv5mI2hyFkuslVkurEtHsHupiPg9uOxgLhyzEQ1sfQp+1feBl80KwfzB8vX2tllYjnDY0ZeRm4OKVixjUZhAAYMbAGXj202dhr2+nG/fMeMJizADCjA7OsPWfW7HiNyv+JcYMKsxMwarruLK10u3jPLHnREzZMQV5xXnUtHLkyBGtz2XVa1jbrtpwU3N4xY3HOw+GPYhnC59F5qVMes6ZZmZaYdViTJkr1qevR1FZEXqs7oHS8lIUXy9Gj9U9kDouFWGNwpTXs+NgMHOfKcIahaFz887oH94fABDdKRqTPpyE7/O+p9/LjB4sn1nFJ1PXoCPNzOjFtDATEMsDNg/VxgjpSHPn5p3p97BriRneWJ6zc1JT/l979x5dVXWuDfzJlQRDIBFDDBASU0ApV5HLR0GCF3pAI1Y4ioJFkHIXikUr3hDGcTjwoBYM6kGPBIcMpbUQejQf0CpFK3KREoXihdAIQgQN4eQCuZBkf3/whWGZT8JK9txr7a3P7z/esbP3u+aea07WXu+as7n9GeD9gxW8sUJKNm43V3l1OYanDce9V98LADhWdgyPbX3sfEETa2f2uSFf0PRN+TcY/9Z4FJ8pBgCs3bcWPZN6Bv39wFOVp1BQUoAhnYd4nYojodjODTmXVJUAAHILc9GtXTckxJhLWwaLXb/ahf2z9iN/Rj7yJuQhNjIW+TPyGx2IgsGorqNQeKoQe4r2AADeP/w+whCG9ARz6cNgoZzdEYr9uai8CJk5mSirLgMAPPnBk7iz550ICwvzOLPm8fvKdViXYXhk2CPIzMlEZHgkUtqkIPcO9x/5aK6CkgJcHnc5oiKivE7FkVBs54ac79x8JyLCItChdQesGrHK67R+cJLjkpE7Phez8mbhdM1ptIpshfV3rEdMZIzXqTVKOUtjurfvjoeGPoRBrwxCva8eQzsPRfbobK/TajYrz7nOHDATMwfMtPFWrhnQcQAK5hZ4nUazhGI7zxwwE6MuG+V1Gi2S1i4NFQ9XeJ2GI9d2uRY7p+70Oo1mUc7uCqX+PGfgHMwZOMfrNPxiZXJtTKdOnYxYcnKyEUtNTTViSUlJRiwyMqDpAjj3MPmFunTpYsSas4NGoKWkmD/xON0lpn379kYsPNzuwl3se3OaM+svsbGxdhKzgPUD1l+io4OnGCMuLs6IsXaurTUrYRMTvXmUin0uGze87C9s7GBjIMPy9mqMcToGenUesv7L5guvBcWWcyIiIj8kWltYRETEMk2uIiIilmlyFRERsUyTq4iIiGWBL791YOjQoUZsyhRzuS4W80dZWZkRGz9+vBE7evSoEevRo4ejz2A5jxw50tHfOuX0OFjOy5Yts5oLw/KbNs3c5YK97vHHHzdiTrYGC4TRo0c7eh37fseNG2fEnFaSumHJkiVGbMeOHUYsLy/P6uceOHDAiLHzjeXHqmnZ+cba3kus77O2XrXKfCbcdt9fsGCBEWPtz/qqG2MHw9qKHQcb71ibBkpQTK7sy2QDrW319eYat0VFRUbsyJEjRszpIwmVlZXNT6yZnB6HV+XqLL/jx48bMfYoTlVVVUByagnWpgw7DvZYSzApKSkxYk6P1x81NeZOJ+ycYWMEW260oiL4n+P89ttvjRgbY9zo+8XFxUaMnZvs8RyvsHZh/YM9Zugm/SwsIiJimSZXERERy1xfRILtWsF2Q5k3b54R+93vfmc1F7YDDtuxgu06w7BdGGztMNOAtQHbJcYptvMG25XEH07b2am9e/caMVu74jRgO374syMM2/WEtYsb2A4z6enOFp8/deqUEfNnNyjW15YvX27EWPux840dm1ftDPBdk/r16+fob233GX9yYbZu3WrEWrLbUFNYzk4/g+3k48a80kBXriIiIpZpchUREbFMk6uIiIhlmlxFREQsC+hzrqyYhxUvMaxYwTZ2s7xPnz5G7Iknngh4Lk453UqOHQcr+mHHZrugyTZWgJCTk2P1M1gxSdu2bY0Ya1MvC2iccFpAyNrUn+IlxmkhGvvOWT+1nZ+/cnNzjRjbvu3w4cMBz4X1S9an2evYcbD2Z2OqP9jnsrmBfa7TYig2BtroR7pyFRERsUyTq4iIiGWaXEVERCzT5CoiImKZtYImVnCwZs0aI8ZWO1q8eLER82c1HKdYcRBb4YUVXbDXBVNxECsEcPo6VgjgzwpIrIhg9erVRmzy5MlGjBV/sH5lu6CJFTSwNmB9iJ0LrLjPq+KbMWPGOHqd0z7kD3aes/ZjsW3bthkx1q+8xFZAYmMH69O2V3djfZX1aacxds6xz7Ddz52+H8uZjcesn9toe125ioiIWKbJVURExDJNriIiIpZpchUREbHMWkETuwHMtrFiW1YxbJUQ2zf42c1ttvpHQkKCEWPbnjldxcQfrECCrbLCsGNzusKQ7S3d2Hfp9PsNCwszYixn29tfsfdjxXhsC0DWN7xayYkVrE2aNMmIubEymdPCMVbww3K2PUY0hhXusAIf9h2z7QzZimpuHUtLsbGNFZ4F0wp3DCsws0FXriIiIpZpchUREbFMk6uIiIhlmlxFREQss1bQxAoT2EowwbQdF8vZaRHBc8895+j9bGNboTFOC3zYKjesKCGYsOIPp9tL+YMVZrDPYMVLy5cvN2K2V8Ji3xsrrmLYuerG6mKsMMiNlaH8xfoC+44ZtuqYG+MiK4b0p63d2G7Rac5urAzVXLpyFRERsUyTq4iIiGWaXEVERCzT5CoiImKZlYImn8+Hezbeg15JvbBgyIImX8tWCfLCbzb/Bn848AckxiYCALq3745149bR1TrYtljsBjorULHpnS/fwcJ3F6K6rhq9O/TGf9/y34hvFU+LapyuPsXYXI3p+Z3PY8WOFYiJjEG3xG5YNmIZEmISaPuxGCuQCHTbN9Y32OeWlpYaMVZwwVbRsnkuvP7p61gdsxphCEPrqNZYMWoFrkm5hrYfayu2ahArkGJ9raX9ZcNnG7Dor4sQHhaOxNhEvJz1MjISM2ibskI+N7YevNDrn76O/9z+n6jqUoWYiBg82OtB/DThpwB4AQ3L5/Dhw0aMFUjZKixsbNwAgHnz5jnKhcXYsdnq0w05V1RV4MqEK7F0yFK0iW4DwPm2oCwX1vcDVcDp95XrZ999hutfux5vHXjLRj6u2X50O94c9ybyZ+Qjf0Y+1o1b53VKTfru9HeYvHEy/nj7H/HFnC9wRbsr8NBfHvI6rSZtLdyKpR8uRe5tufhgwge4Me1G/Ppdb/a3bY5Q6xtfFH+BB/78ADZN2IT8Gfl49NpHcdu627xOq0mVZysxccNErL9jPfJn5COrWxbmbprrdVpN+n47rxuxDlO7T8WC3U1fTHgtFMeN7+f83i/eQ2qbVDz996e9TqvZ/J5cV+5eialXT8W/9/h3G/m4orq2Gnu/2YunP3wavV7shbG/H4sjpUe8TqtJWw5twYCOA9D10q4AgJkDZmLtvrXw+XweZ9a4Pd/swQ1X3ICObToCALJ+koVNhZtQU1fjcWaNC8W+0SqyFV7JegWXt7kcAHBNyjU4XnE8qNu5zlcHn8+H0qpzV6kVNRWIiYzxOKumXdjOP233UxRXFeNs/VmPM2tcKI4bF+Y8sftEbPznxqDOmfF7cs0enY27et1lIxfXFJUX4br06/Af1/0HPp3xKQZ3HIwxb44J6i/v67Kv0Tm+8/l/d4rvhLLqMpTXlHuYVdMGdRyE9wrfw5Gyc5PT2gNrUVNXg5KqEo8za1wo9o20dmm4qdtNAM7dorl/8/24pfstiI6I9jizxsVFx+Glm1/CkFeHIOWZFGTvzsbSG5Z6nVaTLmznZ/Y/g+HJwxEVHuVxZo0LxXHjwpyTWyej/Gw5Ks5WeJhV8/0oC5rSE9KRNyEPPZN6IiwsDAuGLMChkkP46n+/8jq1RtX76hEGczeYiLAID7JxZliXYVg0fBHufvtujHhjBMIRjoSYBESHB++gH4p9o8HpmtO4/a3bUVBSgFduecXrdJq078Q+LNm2BAdmHUDRb4rwyLBHMPb3Y4P6PzENTtecxoMfP4ivT3+NRf0WeZ1Ok0Jx3AjFnBlrKzQ5xW4ys2IFVhRiawumT098ik+Of4Jevl4Azv0vtK6uDl9+/iWm3zndeD0r4mA3wQO5Ikhq21TsPLbz/L+PlR1DQkwCLom+xHFhASvIWbQocINDeXU5hqcNx/WJ1wMAjp8+Dp/Ph7CqMNwz+R7j9Wy1KLaaDTs2W6sJNfSNu/vcfT7mgw9REVH49VzzM1hRDbN69WojZrO/HCk9gqw3snBV+6uwddJWxEbFAnC+Ag9rP1bEws7BlhSEbD60GT9L/RkyEjMAALMHzMb8zfNxsvIk3c7R6WpbrDCLrT7VUt9v5z/d86fz7QzwohrWrqwNna7g1dwVkJoaNxrLj/UZdmz+N3UnYgAAGIlJREFUrHDXlO/nnJaWhsP/exgJMQno0bUHAH7OOS06dfp92PCjvHINDwvH3E1zcez0MQDAH776A7q27YoOsR08zqxxIzNGYsfRHTh48iAA4KWPX8KYK+0NGoFQVF6EzJzM8z9Brdy3EllpWXQ/1mDR0DcKTxUCAF78+EX07tAbneI7eZxZ48qry5GZk4nbrrwNb457818G/GB19eVXY9tX23Ci4gQAIPfzXKS3S0f71u09zqxxodjOoThuhGLOjOtXrsGgZ1JPPD/qecz7yzzU++qRFJuEp/o/5XVaTUq6JAmrx6zGuD+MQ01dDTISMvDaL17zOq0mdW/fHQ8NfQi/yPsF6n31GJA0AIsHmZuLB5OGvpH1RhbqfHXoFN8Jb4x9w+u0mpS9KxuHSw9jw+cbsOHzDefj7/7yXVza+lIPM2vcdenX4YEhDyBzTSaiI6KRGJuIjePNq85gEortHIrjRijmzFibXHNuzbH1Vq6Y2Hsietb39DqNZhnddTRGdx3tdRrNMmfgHNycdLPXaTTLxN4TMbH3RK/TcGzhsIVYOGyh12k02+yBszF74Gyv03AsVNs5FMeNUMz5QkFx5ZqcnGzE4uPjA/650dFmYU2nTubPf+3bmz9VhYcHzy/qLJfU1FQjxtrUjZ0jIiPNbsa+c3Z/lX0f7G/Zd2kb6wcsZyYuLs52OlYlJiYaMdaH2OtsY98ly6WsrMyIxcYG10+17NxMSkoyYqwfsX5uGxsT2Oe2bt3a0d/GxAT+cSr2HbP282q8axDmC4XyPBERkRASPJdfIiIiPxCaXEVERCzT5CoiImKZJlcRERHLXK8WZhV+o0ebJdes0uvNN9909Dp/HDhwwIhNmzbNiB09etSIscrWv/3tb3YSa6YFC8zdOrZs2WLEWJv26NHDai6srZy26ciRI43YuHHjjNjgwYNbmB3H+un48eONGMs5Ly/PiLG+4RV2bL179zZiq1atMmLs+7CNtSnru2xlKNbObowbjVmyZIkRe/XVV40Yy9F2n3aKjR2sz7D+4QY2X7Bxm33HLOdAtbPrk2t9fb0RYycTK5lmf2tbTY25kwjLj+3JGEyKi4uN2JEj5u4u7Hhtq62tNWJFRUVGjOXHjqOqqspOYk1gfc1pzux4gwk7NtafKysr3UjHwNqPDe6s7dmjL26MG40pKTE3qWBt7Uafdoqdc2wpQa+w85C1KdtD2c121s/CIiIilmlyFRERscz1RSTYzwsJCQmO/pbtlsF2a/CHP+/3ySefGLHCwkIjxnZ18Qe79zR//nwjxnYHcbprim1s9xL2Mw7bxeKrr74yYmwHEX/ame005HTnHVs7xwQKa3u2q4hX/YV9byzGzlWnu2mxHX+ag41j7D3Zbjd9+vQxYixv2+MEw86bfv36GbFJkyYZMXaOuIGdh+w2ImtTNkYHal7RlauIiIhlmlxFREQs0+QqIiJimSZXERERy6w958qKJFavXm3E/vrXv7b4M2wXLzEsP3az3GmhjRtFCaywgBUHeVWAwLBiGYa1HytKsN32rNDD6XfuTx93w3PPPed1Ck1i7ef0u2TfWyCwsYg9a8m2QmPFY26ME4zTArVgGjucFgc6PTbW31TQJCIiEoQ0uYqIiFimyVVERMQyTa4iIiKWWStoYgU07EZxaWlpi9/PDWyVFVYkwWLs2NjqMLaLA1hRTWZmphFjhVnBjhXKDR8+3Iix4/XHiBEjjBgrTnG6WowbWKEHK+rYtm2bo/ez3aZO+VPcw1ZOslEsVFZW9i8bALDPYWMWW6GJFTSx93NjVS82RrN+HuxY+7HCRzZ2OF15rbl05SoiImKZJlcRERHLNLmKiIhYpslVRETEMmsFTaz4wem2TIsXL3b0OtvYzXxWgOAUW3UoUDfLv48VUrEiJ9amwVSQw7DtudzIj32XrIiNFQexlcncwFaVYecgw47DjRXRnG5/5w+nW1o2JT4+/l/+vWbNGuM1LG+nx+LVOcfGCbbSFMuPFVyxAk43sPxYgZmbK2HpylVERMQyTa4iIiKWaXIVERGxTJOriIiIZWE+n8/n5geyG96sOGDv3r1GzKst59gqNyzmz1ZZ/mA38/1ZCSuYChVYERZre9tbjbFCm0mTJjn6W1YkEkxFYqyIjRXyFRYWGjHb/Zm1FVtZh+XCVjpjBVxunZfsWFhBEzu+U6dOGTE3+gwrRGXFbWxlI3bOBfvYwfJzWvTXXLpyFRERsUyTq4iIiGWaXEVERCzT5CoiImKZlRWafD4f7tl4D3ol9cKCIQuafK3TQgJ2s9xWQdNrn7yGZz969vy/S6tLcbTsKI7OP0rzY4UT7CZ9oIuX9p3Yh/v+730orS5FRFgE/uvm/0L/lP60aIIVibGtpNhqLKwQ4MLCh8jISHTq1OmiOWfvysbKnSuBMCC9bTqWX78cl7W+jBYlsYIQ1vaBbufXP30dD//jYQBAdHg0JiRMQHqrdFokxgrC/FmVh7WLU07Pw0AVcDRHU+cgWx3L6WpvrN8Hor/kfp6LuzfcjfKF5U1+jtPPDmTxUmPjBsD7KitoYsfh9DtpSUFT9q5svPjxiwhDGDISM/By1stIuiQJgPMtQFnxEjuH2etsrKzn95XrZ999hutfux5vHXjL72Tc8ss+v0T+jHzkz8jH7l/tRnJcMrJHZaNDXAevU2vUmbNnMPL1kXjwZw9i7/S9eOzaxzBh/QSv02rSnqI9WLZ9GTbdvgkfTfwIGe0y8ORHT3qdVpO+KP4CD/z5AdyfdD+WpCxBVtssZH+X7XVaFxVq52EonoMNDp48iAVbFsDlBy1aJJTHje1TtmP/rP3omtgVj733mNdpNZvfV64rd6/E1KunIrVtqo18XLf0w6VIuiQJ06+Z7nUqTdpyaAsyEjIwuutoAMAt3W9BekK6x1k1rX9Kfxy87yBOl59GVW0ViiqK0CU+uDdibhXZCq9kvYLvtn8HAEiPTkdpXSlqfbUeZ9a0UD4PQ+UcBM5NVhM3TMSzP38Wd/3xLq/TuahQHjeiIqJQVVuFY+XHkN4uuHNm/J5cs0ef+1/9lkNb/E7GbcVnivHMR89gz7Q9XqdyUV+e/BLJccm4d+O9+OTEJ2gX0w5P3/i012ldVFREFN459A7m/mUuWkW0wsODH/Y6pSaltUtDWrs05GzPgc/nwxun3kC/1v0QGWZtj4uACNXzMJTOQQCY/vZ0TO8/Hb079PY6FUdCedzI/TwXU/80Fa0iW2FJ5hKvU2q2H3VB06o9qzCm+xhckXCF16lc1Nm6s8g7mIdp/afh42kf476B92H02tGorq32OrWLuinjJhyafgi/HfxbjM0di3pfvdcpXVR1fTVeKH4B3579FpMvnex1Oj9YoXQOvrD7BUSGR2JKvylep+JYKI8bt155K4ofLMYTw5/Az1//eUiMG9/n+n/H2c1tVsTBbiiz1UT8KVZY9491WPFvK/4lxopq2Ge4sSXe96W0ScFVl12FQZ0GAQDGXDkGU/9nKv556p/0hjwrkGDFQQwrHGtJOxeUFOB4xXF0qj9X+HRzys24v/x+HD5xmObC2p7l4vQ4WupI6RE8X/k8unXqhj/d+CfERsYC4P2U5cwKQhi26o0b2LnFCkLYSkK2i4PYOciwcYPFWDGULTn5OThz9gz6vtQXNXU1qKytRN+X+iJvQh5S2qQE7HP90dS4cdVlV9G+wPq001WubGwX2DBuDE0dCgCY0m8KZrwzA6cqT+HS1pfSecDpinSsfwRqBakf7ZXrqcpTKCgpwJDOQ7xOxZFRXUeh8FQh9hSd+/ns/cPvIwxhQX3/5JvybzD+rfEoqSoBAOQW5qJbu25IiPF/f81AKa8uR2ZOJrIysvDqqFfPT6xiX6idg7t+tQv7Z+1H/ox85E3IQ2xkLPJn5AftxAqE9rhRfKYYALB231r0TOqJS1tf6nFmzRPcN5ICqKCkAJfHXY6oiCivU3EkOS4ZueNzMStvFk7XnEaryFZYf8d6xETGoApVXqdHDesyDI8MewR3br4TEWER6NC6A1aNWOV1Wk3K3pWNw6WH8faht/H2obfPxzfeZq41LP4JtXMwFDU1bgSrhnEjMycTkeGRSGmTgtw7Wv6ImlesTa45t+bYeitXDOg4AAVzC7xOo1mu7XItdk7d6XUazTJzwEyMumyU12k4tnDYQiwctjAongVtiVA6D0PxHGyQ1i4NFQ9XeJ2GI6E6bswcMNPrNPzi+pVrZKT5kSkp5s8q9fXmzWv2t7bFxJj/o2P5hYcHzy/qLJfExEQjlppqPqZRVlZmxJKSkuwk9v+x7y05OdmI1daaj7uwXNxoe/YZLJeamhoj5nRiZm3ghujoaCPG+nhsbPD8JM7anvXxYOO0zwQT1q6sf7Bji4uLC0hO3+d0HGPcHE9c33JORETkhy54Lr9ERER+IDS5ioiIWKbJVURExDJNriIiIpa5Xi3MqrqmTZtmxAYPHmzEpkwxlx2Lj49vcS47duwwYgsWmFt1HT161NH7sZxXrTKf6/QnZ2bJEnPdTXZsrO0ff/xxIzZy5Eg7iTUT6wdbtphr5bI2dSPnAwcOGDGnbT9u3DgjtmzZMjuJNZPTdmar99jYiuv7WJ9kK469+uqrRoydR6wfeNXOAO8zrP3z8vKMmO1xgo1jo0ePNmJOK2979OhhxNi56WRryuZg+Q0dOtTR57J2DhTXJ1f2iM3x48eNGHucgf2tP6qqzMUXWAdke54ybDkw2zkzJSUlRqyoqMiIsTatrKwMSE4t8e233xox1vZe5cweoWB9l+VcXFwckJxawmk7u/GsLzs/WH9m+bG9dIOpnQHeZ9gY48Y4wR51O3LkiBFzupQgW2KVfYZtrK3YcXj9uKR+FhYREbFMk6uIiIhlrv8szHbfYLuIsJjt+z3sZ1wWY5/L7guxnNlOKv7swsB2p1i+fLkRmzRpkhFjbc92sfBqXRH2M5NX2M4grK1Yf2G73bD3cwPbQWjjRnOd5D59+hgxGzucXAw7P1h/XrRokRFj/WX+/PlGjJ2/bLelQGDnuu2dhZxi7eW0T7N+xHZNYq9zYwcx9lM2y4/1/UDtpKQrVxEREcs0uYqIiFimyVVERMQyTa4iIiKWBbSgiT0n57Qoid1ktl3wwm7cs4IhdhOcPXfHioj8KV5iMjMzjRgrRmGFIk7bj31vttuetfOaNWusfoY/WDEEa3tWwMG+c6/2h3VaSMUKW9wo+mFtxfoua2d2DrJxw60CIlbkyPoROz72Pdluf3YOs3ZlFi9ebMTYc8a2i04ZpzkzgSpeYnTlKiIiYpkmVxEREcs0uYqIiFimyVVERMQyawVNrGCDFSuwG/xO388r7MY949aqLxdiKy+xGCsiYiva2C6uYMUyrEjMqS5durT4b51ihQ8sxvopa2fWBqydbRffsOIZxnbhnVOs/Vi7sOKl1atXGzG3joOdX2wlIqcFNP369TNihYWFRsyr1Z0YVrzkxiprTguaWKGnm3TlKiIiYpkmVxEREcs0uYqIiFimyVVERMSygBY0sRjbOoqt/hFMN+6dCqYt01gBktMiMduFWZMnT3b0GU5X4fGqnVnBCuu7DNtGjX0f/qw+wzhdoYkVGrpxDrJVjZzm7FUBIcD7Jdv2jGGFWAwrjPOq8IwVEbqxvZzTVa+Cka5cRURELNPkKiIiYpkmVxEREcs0uYqIiFhmraDJ6fZtDLtpHUwFTaxwgq3axFbD8aoAgXFaAMJWn/GneMTpake2i3lsc9on2cowrB/Y3p6LFQI5LbJhK0ix/mx7yy62MhErWGP5seIZpytS+Yu1AytUYn1627ZtRoz1GS8Lti7EcmGrrNnerpKdN063BfWarlxFREQs0+QqIiJimSZXERERyzS5ioiIWGaloMnn8+GejfegV1IvLBiyoMnXspvRrOgiMzPTRmqNyt6VjZU7VwJhQHrbdCy/fjkua32Z4xVjWM6BXjno+Z3PY8WOFYiJjEG3xG5YNmIZEmISaKHNmjVrjNjevXsdfY7t43DaP1jRBFsJJ5Be++Q1PPvRs+f/XVpdiqNlR3F0/lFaXMFWn2KrNtkuBLrQvhP7MPMvM1FWU4aIsAg8d91z6NuhLyZNmmS8lvUNZt68eUbM5nFs+GwDFhUtQnhYOBJjE/Fy1svISMygObO+wbZLtF2M15jcz3Nx94a7Ub6w/HyM9Q9WiMXOV/a3tvLO3pWNFz9+EbVna5HaJhVP/Z+n0D62PQA+zrLz3+mqSDa3q/T5fJj313nGuMHGaFbQ5PVKTn5fuX723We4/rXr8daBt2zk44o9RXuwbPsybLp9Ez6a+BEy2mXgyY+e9DqtJm0t3IqlHy5F7m25+GDCB7gx7Ub8+l271aaBEGr945d9fon8GfnIn5GP3b/ajeS4ZGSPykaHuA5ep9aoM2fPYOTrIzG3/1y8f9f7eGDgA5i2eZrXaTWp8mwlJm6YiPV3rEf+jHxkdcvC3E1zvU7LkYMnD2LBlgXw+Xxep3JRDWPd9inbsXnMZqS1ScOz+c9e/A89FmrjBuP3levK3Ssx9eqpSG2baiMfV/RP6Y+D9x3E6fLTqKqtQlFFEbrEB34Dbn/s+WYPbrjiBnRs0xEAkPWTLMx7dx5q6mo8zqxpodg/Giz9cCmSLknC9Gume51Kk7Yc2oKMhAyMTB8JABh1xSikxgd3e9f56uDz+VBade4XoIqaCsRExnic1cWdOXsGEzdMxLM/fxZ3/fEur9O5qIaxLioiCsfrjuPEmRPo3Kaz12ldVCiPGw38nlyzR2cDOHeCh5KoiCi8c+gdzP3LXLSKaIWHBz/sdUpNGtRxEFbsXIEjVx9Banwq1h5Yi5q6GpRUlXidWpNCtX8UnynGMx89gz3T9nidykV9efJLJMcl474/34f9xfvRtlVbLB7qbEMBr8RFx+Glm1/CkFeH4NLYS1Hnq8OHUz70Oq2Lmv72dEzvPx29O/T2OhXHoiKikPt5LqbkTkF0eDTm9zN/Tg82oTpufN+PuqDppoybcGj6Ifx28G8xNncs6n31XqfUqGFdhmHR8EW4++27MeKNEQhHOBJiEhAdHu11aj9Iq/aswpjuY3BFwhVep3JRZ+vOIu9gHib1moStd27FtD7TcPvG21FdW+11ao3ad2IflmxbggOzDqDoN0V4ZNgjGPv7sUH9U+sLu19AZHgkpvSb4nUqzXbrlbfi7+P/jnl952HSnycF9Vj3Q2FthSan2M1ytjpJIBWUFOB4xXGc3HsSANDZ1xlfl32Nd957x/EWYmyFJnaj3Zby6nIMTxuOe6++FwBwrOwYntr5FNKT0+lKNawohBVhsaIVr1bHcroKjBtbXa37xzqs+LcVF82F9d1AFy9dKKVNCq667CrccOUNAIC7+t+Fee/NQ4mvhLYVKzphqwY53aKwJTYf2oyfpf4MGYkZAIDZA2Zj/ub5OFl5khbZsOIltn1lIFc1ysnPwZmzZ9D3pb6oqatBZW0l+r7UF3kT8pDSJoX+DWtDp6tm2dAw1g1NHQoAuP0nt+PRHY+itLoUCTEJND9WRMi2nGPbKLqxqhSbQ1h+7DjcKngDfqRXrt+Uf4Pxb41HWW0ZAOD9U+8jNSYV8ZHxHmfWuKLyImTmZKKs+lzOT37wJO7seSfCwsI8zuyH51TlKRSUFGBI5yFep+LIqK6jUHiqEHuKzv2E/f7h9xGGMKQnpHucWeOuvvxqbPtqG05UnABwrvo2vV062rdu73Fmjdv1q13YP2s/8mfkI29CHmIjY5E/I7/RiTUYNIx1xWeKAQC5hbno1q4bEmISPM7sh8/1K9dgMKzLMDwy7BE8+t6jCEc4EqMSsTB9oddpNal7++54aOhDGPTKINT76jG089Dz9yXEroKSAlwedzmiIqK8TsWR5Lhk5I7Pxay8WThdcxqtIlth/R3rg7pA6Lr06/DAkAeQuSYT0RHRSIxNxMbxwbc+bKhrGOsyczJRX1uPDq07YNWIVV6n9aNgbXLNuTXH1lu5YuaAmUgpCt7/cTJzBs7BnIFzvE6jRUKpfwzoOAAFcwu8TqNZru1yLXZO3el1Gs0ye+BszB442+s0WiStXRoqHq7wOg1HZg6YiZkDZtLbAcEulMaNC7l+5RodbRbgpKSYk1xMTOD/1x0bG2vE2G/3THy8+RNyeLg3v7KzXFJTzRL2srIyI5aYmBiQnFrC6b2UQC/W0RjWX1jfDXbJyclGjLUz61duSEpKMmLB1A+aIzLSHGK9amuWi9NxolOnTkaMnQ9uYOMsy49h80+ghPmCuTxPREQkBP0oC5pEREQCSZOriIiIZZpcRURELNPkKiIiYpkmVxEREcs0uYqIiFimyVVERMQyTa4iIiKWaXIVERGxTJOriIiIZZpcRURELNPkKiIiYtn/A7OPii8c5ea9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x576 with 100 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, axes = plt.subplots(10, 10, figsize=(8, 8),\n",
    "                         subplot_kw={'xticks':[], 'yticks':[]},\n",
    "                         gridspec_kw=dict(hspace=0.1, wspace=0.1))\n",
    "\n",
    "#digits.images[i]为一个8*8数组，ax.imshow将其值转化为灰度形成模拟数字图\n",
    "#digits.target[i]为图对应的实际值\n",
    "for i, ax in enumerate(axes.flat):\n",
    "    ax.imshow(digits.images[i], cmap='binary', interpolation='nearest')\n",
    "    ax.text(0.05, 0.05, str(digits.target[i]),\n",
    "            transform=ax.transAxes, color='green')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to work with this data within Scikit-Learn, we need a two-dimensional, ``[n_samples, n_features]`` representation.\n",
    "We can accomplish this by treating each pixel in the image as a feature: that is, by flattening out the pixel arrays so that we have a length-64 array of pixel values representing each digit.\n",
    "Additionally, we need the target array, which gives the previously determined label for each digit.\n",
    "These two quantities are built into the digits dataset under the ``data`` and ``target`` attributes, respectively:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797, 64)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = digits.data\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797,)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = digits.target\n",
    "y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see here that there are 1,797 samples and 64 features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unsupervised learning: Dimensionality reduction\n",
    "\n",
    "We'd like to visualize our points within the 64-dimensional parameter space, but it's difficult to effectively visualize points in such a high-dimensional space.\n",
    "Instead we'll reduce the dimensions to 2, using an unsupervised method.\n",
    "Here, we'll make use of a manifold learning algorithm called *Isomap* (see [In-Depth: Manifold Learning](05.10-Manifold-Learning.ipynb)), and transform the data to two dimensions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797, 2)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.manifold import Isomap\n",
    "iso = Isomap(n_components=2)\n",
    "iso.fit(digits.data)\n",
    "data_projected = iso.transform(digits.data)\n",
    "data_projected.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the projected data is now two-dimensional.\n",
    "Let's plot this data to see if we can learn anything from its structure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data_projected[:, 0], data_projected[:, 1], c=digits.target,\n",
    "            edgecolor='none', alpha=0.5,\n",
    "            cmap=plt.cm.get_cmap('Spectral', 10))\n",
    "plt.colorbar(label='digit label', ticks=range(10))\n",
    "plt.clim(-0.5, 9.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This plot gives us some good intuition into how well various numbers are separated in the larger 64-dimensional space. For example, zeros (in black) and ones (in purple) have very little overlap in parameter space.\n",
    "Intuitively, this makes sense: a zero is empty in the middle of the image, while a one will generally have ink in the middle.\n",
    "On the other hand, there seems to be a more or less continuous spectrum between ones and fours: we can understand this by realizing that some people draw ones with \"hats\" on them, which cause them to look similar to fours.\n",
    "\n",
    "Overall, however, the different groups appear to be fairly well separated in the parameter space: this tells us that even a very straightforward supervised classification algorithm should perform suitably on this data.\n",
    "Let's give it a try."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Classification on digits\n",
    "\n",
    "Let's apply a classification algorithm to the digits.\n",
    "As with the Iris data previously, we will split the data into a training and testing set, and fit a Gaussian naive Bayes model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "model = GaussianNB()\n",
    "model.fit(Xtrain, ytrain)\n",
    "y_model = model.predict(Xtest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have predicted our model, we can gauge its accuracy by comparing the true values of the test set to the predictions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8333333333333334"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(ytest, y_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With even this extremely simple model, we find about 80% accuracy for classification of the digits!\n",
    "However, this single number doesn't tell us *where* we've gone wrong—one nice way to do this is to use the *confusion matrix*, which we can compute with Scikit-Learn and plot with Seaborn:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQ8AAAEPCAYAAABcL0E+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzt3XlcVPX3x/HXMMPqRogbLulXTc19ScNdzB1FBHFLc0kz9yULTc19y9wKzcrcTXDFNU1NUyDTMpfMckNccENBVGBgZn5/8BOXUOTOvcjoeT4ePh7NwLzv8SaHmXvv51ydxWKxIIQQmWT3ogsQQtgmaR5CCEWkeQghFJHmIYRQRJqHEEIRaR5CCEWkeQghFJHmIYRQRJqHEEIRaR5CCEWkeQghFJHmIYRQxPCiC3ge98YEqJ6ZZ3qY6plC2KoU4+VMv0beeQghFJHmIYRQRJqHEEIRaR5CCEWkeQghFLHZ5mGo1QzngV/gPGAmjp1HQI7cOHYchlO/GWl/XD5dgmOXj63aTssWjfnj95/468QvrP5hIbly5bS6di0ybS3Xlmq1tVytan2STTYPO48S2NdpTcI3o0n46iMsMVdxaNyBpNWzSJz/MYnzP8YYuhBL4j2Mmxcp3o67uxvffTuLgA59KF+hPufPX2DK5FFW1a5Fpq3l2lKttparVa3p0ax5nD17lvnz5zN27FjGjRvH/PnzOX78uCrZ5ivnSZgzGJISwGCPLrcblvt3H36DXo9ju/4Yty3BcidG8XaaNGnA4cNHOXPmPABfL1xG506+VtWuRaat5dpSrbaWq1Wt6dGkeaxcuZJhw4YBULFiRcqXLw/AmDFj+P7779XZiNmEvtxbuHy0ALvi5Uj54+e0LxmqeWGJv4Xp70NWbaJoEQ8uXrqS9vjSpWjy5Mlt1dtALTJtLdeWarW1XK1qTY8mV5guW7aMjRs34uzs/NjzPXr0wNfXl549e6qyHdPfh7j/9yEM1Rvj9N6nJMwZBBYL9rVbkRT6jdX5dnZ2pHdnCpPJlK0ybS3Xlmq1tVytak13W6onAgaDgZSUlP88n5iYiL29vdX5OrcC2BUrk/Y45Y896FzzgVMO7AoVBzs95siTVm8n6uJlPDwKpD0uXLggt27d5v79hGyVaWu5tlSrreVqVWt6NGkeffv2pW3btowePZq5c+cyb948Ro8eTfv27enbt6/V+bpcr+EYMARccgFgqFwP8/UoSLiLXfE3MZ07YfU2AH76aR+1alajVKkSAHzQpyubNu/Mdpm2lmtLtdparla1pken1R3jrl27RkREBNevX8dsNlOwYEE8PT0pUKBAxi9+QnoL4wxvNcG+VjMsZjOW+FsYNy/CEnsDB+9eWOJvk7xv/TMzn3dhXIvmXkyaNBIHB3vOnb1A956DuX07NtN/B60zbS3Xlmq1tVwlmUoWxmnWPNQkq2qF0JasqhVCZBlpHkIIRaR5CCEUkeYhhFBEmocQQhFpHkIIRWziVK3BobDqmXemtVQ9EyB34DZNcoVtKZwrrya5l+OVL/R8FjlVK4TIMtI8hBCKSPMQQigizUMIoYg0DyGEItI8hBCKvDTNQ62J0YYqjXDqNh6nbuNwaNMfnHOBkwsOrfrg1H0iTl1GY6jilS1qteVcW6pVy1yApi0b8deFCNXyZHp6Jqg1MVqXvxiG6k1JXD2NxGXjsMRew76ODw4NOkByEolLx5L4w1T0JSpgV6LSC63VlnNtqVYtcwGK/68Yn44fjg6dKnkvxfT0rKTWxGjL9SgSF48GYwLoDehyvgYJ97Ar8DopJyPAYgGzCdO5YxjeqP5Ca7XlXFuqVctcJ2cn5nw9lYljZlqd9YDNT0/PaqpOjDab0JesgnOfGdgVKU3KX2GYr57H8KYn2OnB3hF96erocuR58bXaaK4t1apl7tRZY1i1ZA2n/vrXqpxH2fz09CtXrjzz6x4eHqpuT+2J0aazf5Kw4E/0Fevh2G4Iiasm4VDPH6d3x2C5F4fpwkn0HiWzRa22mGtLtWqV27VnB1JSTISs2kiRour9PGTl9HRNmscHH3xAZGQk+fPn/89fRKfTsXv3blW3F3XxMjVrVk17rHRitM41HzqXPJivnAHAdOIADo3fRWfvhHH/Wki8D4ChZkvMsddfaK22nGtLtWqV69+pDc7OzmzbG4KDgz1Ozo5s2xtC9479uX71Rraq9Wk0+djyww8/UKJECWbMmMGePXse+6N24wD1Jkbrcrji0KoPOKW+xdOXfRtLzGUMlRpgX9sn9ZtccmGoUBfTqd9eaK22nGtLtWqV69OkC03rtqNlwwC6d+hPYkISLRsGWNU4tKr1aTR555EzZ04mTZrEmjVrqF5d2YHFzLhxI4b3ew8jePU3j02Mzizz5dOkHNyKU8BHYDZjuRdLUuh8LAnxOLTohVO3cYCO5IhQzNciX2ittpxrS7VqmauFrKxVluSrTJbkC5Al+UII8VTSPIQQikjzEEIoIs1DCKGINA8hhCLSPIQQiryyp2qruiu7vDwj/XTFVM8cFver6pkA8Ub1rzoUqYZ41Nckd86VXzTJlVO1QogsI81DCKGINA8hhCLSPIQQikjzEEIoIs1DCKGIJkvyX4SWLRozaVIgjo6OHD/+N737DCc+/q51mf7N6PJBQNrjHLlyUqBQPlpV9+PWzduZyvpfuzpU/LAlFguYEpL4dcxyYo6dp+KA1pRqXw87vZ6z68P4c9Z6xfUGdPBh4JD3sVgsJNxP5JMRE/jzyAnFeQ9osm81yLSl3I5ffEj0qSj2fbsVgPF/fEPs1YcrZvcu3MKR0LBsUevTvBTXebi7u3Hsz5+p37AtZ86cZ+qUUeTMmZOBg54+NTqz13noDXq+Wf8lW0J+ZMOKTU/9vvSu88hdshAt1nzKpuajSbgeSxGvynhO60FE4GKqfdKebW0nYjGbabryY/5euovIzQcfe/3zXOdRqnQJNm9fScM6Ply7doMmTRswa+5EKpZ7+vUGz3Odh5J9+yIys1vu067zyF/Sg3YTe1KsSkl2zF7Lvm+3ku9/hej53Qimew3LsJaMrvNQug+y1XUeu3btYvny5URFRT32fHBwsOrbyoqJ0e/178LtmNhnNo6nMSclEzbiOxKuxwJw8+h5nPO5Uty7Fuc2RJCSkIQpKZnTIb9Qsl0dRfUlJRkZ3H8U166lTqI6cuQ4+Qu4Y29vryjvAZmerm5unW5NObh6D0e3PfwFUbz6G5jNZvqFfMaw7dNpMqgdOjtlt2Kw+enpM2fOZMWKFURGRtKpUydCQ0PTvrZ69WrVt6f1xOg8bnno8kEHZn/2paLX3710k0u7/0x7XPOzLlz86Q+c87ty78rDt6r3om+Ro5Cbom1cjLrMzh170x5Pnvop27ftITk5WVHeAzI9Xd3cDZ8t4cim8Mees9PrOX3gBN+9N435HcbzRv1K1O3e/IXXmhFNjnns27ePDRs2YDAY6Nq1Kz179sTBwYEWLVqkO9nZWlpPjPbt0ppfdhzgclS0VTkGZ0fqzulDDo+8/NRlBg2/Hgg8rFuHDrPJbNU2XFycCVo4g8KFC+Hv28OqLJDp6VrmPnBw9Z6HDxLgl++2UbdHc/Z/vz3TWVk5PV2Tdx4WiwWdLvVtV/HixVm4cCGTJ0/m4MGDac+rKeriZTw8CqQ9VntidJM2XmwOtm68YA6PvLTaNBaLycyP7SdjvHOfe5djcC7wWtr3uBR8jfvRtxRvo0iRQuzYHYLZZKJNyy7ciYu3qmbQZt9q9f/L1nIfqOZbl0JlHx4r0+nAlJyiKEvrWh+lSfNo3rw5Xbt25dixYwCULl2auXPnMmTIkP8cA1GDlhOjc+XJSdEShTl6WPlZC0MOJ1qs/ZQL2w6zr18QpsTUjxJRO3+npG9tDM6O2DkYKBVQj6gdvyvaRs6cOdi8fSWbN+2kV/chJCYmKa73UTI9XfuJ5IXKFKXZMH90djoMjvbUea8ZR7coWwxp89PTBwwYQPXq1cmRI0fac9WrV2f9+vV8//33qm9Py4nRRYsX4ea1GEwpyt/2levRhBxF3CnWogbFWtRIe35Hh6lc2H4Y763j0TsYiNrxO2fW7Fe0jd4fdKVoscJ4t26Cd+smac/7eHfj9q1YxbXL9HTtJ5LvnLMO3wk9+GjHDOwMeo5tO/j4R5lsVOujXopTtUrIknxZkq8lWZIvhBBPIc1DCKGINA8hhCLSPIQQikjzEEIoIs1DCKHIK3uq1pbITbm1Y6/XZipF0Zz5NMk9F2fdEomnkVO1QogsI81DCKGINA8hhCLSPIQQikjzEEIo8tI0j5YtGvPH7z/x14lfWP3DQtUmJ2mRq2amoUojnLqNx6nbOBza9AfnXODkgkOrPjh1n4hTl9EYqnhlm3q1zNQy94Fvv/2CIUP6qJYXOH4Ie49sIfTnlYT+vJI5306xOlPrffDAS9E83N3d+O7bWQR06EP5CvU5f/4CUyZbN/RWq1w1M3X5i2Go3pTE1dNIXDYOS+w17Ov44NCgAyQnkbh0LIk/TEVfogJ2JSq98Hq1zNQyF6BMmVJs3/4Dvr7qnjav+lYlhvYZhU+jLvg06sKQ3tlz36ZHs+YRGRnJtWvXAFizZg2TJk1i2zZtriuwhcG3WmRarkeRuHg0GBNAb0CX8zVIuIddgddJORkBFguYTZjOHcPwRvUXXq+WmVrmAvTt240lS1azfv1WVfIA7B3sebNiGXoP6Mbmfav5cvEMChUukPELnyErByBrcoXMkiVLWL58OWazmbfffpvo6GiaNGnCunXrOH/+PP3791d1e88a+mrN/Sq0yFU902xCX7IKDk27YTGlkBQeii5HbgxvemK8chb0BvSlq4NZ2TAjm9gHGucCDB06FoDGjetZlfOoAgXz8euBw8yeuoDTp87Sq39XFiyfRVuvLooztdwHT9Kkeaxbt45t27Zx8+ZNvL29+fXXX3F0dKR9+/b4+/ur3jxsafCtFpmms3+SsOBP9BXr4dhuCImrJuFQzx+nd8dguReH6cJJ9B7Khh/Zyj7QMlcrl6Ku0LvTwylfi4KW0394L4oU8+BS1JVnvPLpbH4AstlsxsHBgcKFC9OzZ08cHR3TvqbFX8KWBt+qmalzzYedR6m0x6YTB9DlzovO3gnj/rUkLhtH0rrZoNNhjr3+wuvVMlPLXK2UebMUPu2fOIai05GscPgxvAQDkJs2bcq7776LyWRi4MCBAJw6dYrOnTvTokUL1bdnS4Nv1czU5XDFoVUfcEo9mq4v+zaWmMsYKjXAvrZP6je55MJQoS6mU7+98Hq1zNQyVytms4XRUz6iSDEPADr38Oefk2e4Fq2s0cNLMAB58ODBHDp0CL1en/acg4MDAwcOpEGDBqpvz5YG36qZab58mpSDW3EK+AjMZiz3YkkKnY8lIR6HFr1w6jYO0JEcEYr5WuQLr1fLTC1ztXL61Fkmjvycr1fMRq+34+qV6wzrY92ZERmA/ARZVSurarUiq2pTyapaIUSWkeYhhFBEmocQQpHnah7Hjh1j9erVGI1Gjhw5onVNQggbkGHzWL9+PSNHjuS7774jPj6efv36ERISkhW1CSGysQybx/LlywkODiZnzpzkzZuX9evXs3Tp0qyoTQiRjWV4nsrOzo6cOR8u6S1UqNBj12/YKq1O0SWblF8d+DRanVK9Uq9Uxt+kgMf+M5rkaqF0Hg9Nck/eitIkNzvJ8J2Hq6srf//9NzqdDoBNmzaRJ08ezQsTQmRvGf76HTVqFIMHDyYqKoq6devi6OjI/Pnzs6I2IUQ2lmHzKFmyJKGhoURGRmIymShRogT29vZZUZsQIhvLsHns3Pn4oprIyEggdfGbEOLVlWHzWL58edp/Jycn888//1CzZk1pHkK84jLVPADOnDnDl19+qVlBQgjbkOnL00uVKsW5c+e0qMUqWk6MVntidnafHO7s44vboiW4fbeYPBMmo3N1TfuaXb585A1eiy63dWfcsvs+eFLpsiVZtD6I4J+W8sOO7ylXqYwqubY0mf5JGTaPnTt3pv3ZsWMHX3zxBQaDNtdIKKXVxGgtJmZn98nhhtJv4BLQgduD+nPr/R6YLl8iZ49eADg1acZrs79E727dcvPsvg+e5OTsyNfBc1gctIIOTd5j4azFTAsany3rzVbT05cvX572Z+XKlVy8eJFp06ZlaiOZ/f7M0mpitBYTs7P75PCU0/8S060Llnv3wN4BO/d8mO/cwS5vXhzq1CU28KNsU2tW5Xo2qMXFyMsc2B0BwN4d+/moz6dW59rSZPr0ZPqYR0ZGjhz5n+f27NlDXFwcAFOnTs1U3vPQamK0FhOzbWJyuMmEQ5265B4+AktyMveWLMIcE8OdcWMU16dZrVmQW7xkUWJuxDBu1ijKlC9FfNxdZk/8SnGelvVmi+npffv2feYLv/7663Sfd3V1ZePGjfTt25fcuXMD8Ouvv1KzZk0rynw2W5qabSuTw41hB7gZdgCnlt64TptJTLfOqfeBUYGt7IMHDAYDdb1q875ff44fOUnDZvUIWjmLZjV8STYmK861pcn06Xlq82jWrJmiwE8++YT69eszZ84chg0bRq1atVi6dCm+vtq8dYLUidE1a1ZNe5ydp2ZrVatauXqPwti5uZF84jgAiT9uI9eQYehy5cJy545VNapda1blXr92k/OnIzl+5CSQ+rFl3KyRFHndg/OnL2SrerPyZ+Gpxzx8fX3T/dO2bVuqVq36tJcB4OnpycKFC1m1ahXTp0/X/B2ALU3Nzu6Tw+3y5iX36LFpZ1OcGjchJfK8ao1DzVqzKvfA7ggKF/NIO8NS/e0qYLFwOcq6eaK2NJk+PRke81i9ejUzZswgIeFh53JzcyMsLOyZr3N1dWXu3LmsWbOGf/75x/pKn8GWpmZn98nhycePcX/lCl6bNQeLyYQ5Joa4sdYfHNSi1qzKjblxiyE9PmH0tBE4uzhhTEpmaM+RGJOM2a7ebDU93cvLi9mzZ7NgwQKGDBnCzz//zNWrVxk/3vpTVc9Li+nptrQkXyuyJB/edCumSa6tLcnXZHq6q6srlStXply5csTExPDhhx9y6NAhRQUKIV4eGTYPg8FAXFwcr7/+OseOHQOy51kMIUTWyrB5BAQE8MEHH9CwYUOCg4Np164dJUsqu2myEOLlkeExD4vFQkJCAi4uLly7do3jx49Tt25dnJycsqpGOeahETnmIcc8HlByzCPDn6AGDRrg7++Pn58fhQsXpkCBAhm9RAjxCsjwY8vixYsxGo107tyZXr168eOPP5KSYju/XYUQ2njuG12bzWb2799PUFAQly5dIjw8XOva0rzqH1tsqVaA+OCBmuTm6qD+HBl3l9yqZwLcvK/eRXVZQZOPLQAxMTFs2rSJDRs2YLFY+PDDDzO9ISHEyyXD5tG3b1+OHDlCkyZNmDhxIpUrV86KuoQQ2VyGzcPLy4svvviCHDlyZEU9QggbkWHzCAgIyIo6hBA2JtMzTIUQAqR5CCEUeq7mcfXqVfbt24fJZOLKlSsZv+AFkOnpD2X3evf8dYHaY1PHWyanmJi4Pox2s9bTbtZ6vtjyGyazOdvU+kDP3p3ZF7GZveGbWLLqK9zd3VTJfamnp+/du5eOHTsyfvx4YmJiaNWqFbt27dKkGKVkenoqW6j3ws04Zm87xIOLi1ZH/M3te4msHeJLyOC2HI26zs5jkdmi1gcqVX6TDwf2xLtZJxrWbsP5sxf4+NNBVue+9NPTg4KCCAkJIXfu3OTPn59Vq1Yxb968Z77mwepbgIiICKZNm8bMmTM5evSo9RWnQ6anp8ru9SYYU/g0+BeGt3o4z7ZrvQpM79wIOzsdcfeTiE8wksfF8YXX+qhjR0/iWa058Xfu4ujoQMFCBbh9K9bqXFufnp5h8zCZTOTPnz/tcbly5dDpdM98zWeffQbAypUrmTJlCgULFsTd3Z2xY8eyYsUKK0v+r2dNjLbG0KFjCQ4Otba8x2hVK2T/eidtCMe/ZhlKF3ztseft9XbM3X4Y78/X4pbTmarFla2f0nLfpqSk0LxVY/44uZe3a9dg9coNVmdqUa+W++BJGTYPZ2dnrly5ktYwDh8+jKPj8/1mCAkJYdmyZXTv3p3u3buzcuVKTZqHTE/Xjlr1Bkf8jd5OR9u33kj364Nb1OCXz7rg8VpOJm9UtvRB633749bdlC9Zm5nTgli9/tsMf4lmxNanp2fYPIYPH07Pnj2JioqiQ4cO9O/fn48+evaNf1JSUjCbzbi6uuLg4JD2vIODA3Z26p/gibp4GQ+Ph7+tsvv0dFupFdSrd9PvZ/jr0k0C5oYycMlPJCWbCJgbypHIa1y4kXpPH3u9HW2ql+LU5ZgXWuuTipcoRs23q6U9/mHFOooU9cDV1bpbbmpRb1b++8rwJ7latWqEhIQwa9Ys+vbty/bt2zO8B4urqysNGzbk/PnzTJw4EUg99tGxY0eaN2+uTuWPkOnp2lGr3pUDWrNuqC8hg334snsTHO31hAz24dDZaD7f8hspJjNms4Vtf57jrZKFXmitTypQMB9fL/oCN7fUe/b6BbTm1N+nuX3buuMeL/309L/++gsAd3d3AKKjo4mOjqZ8+fJPfc2Du8ydO3eOO/8/st/BwYFBgwbRsGFDa2v+D5merh2t6+3RoCIztvxGwNxQ7HRQtXgBBjWvka1qPRjxO3O/WMj6LctIMaVwLfoGPboMsDr3lZie/kBycjI3btygQoUKrF27VpOC0iNL8m2nVpAl+SBL8oHU+8w+6uDBg2zevDnTGxJCvFwyffSyVq1aaR9lhBCvruc+5gGpw5BPnDhBYmKipkUJIbK/DJvHwIEPP7/qdDry5s3LuHHjtKxJCGEDMmweo0aN4p133smKWoQQNiTDYx6zZ8/OijqEEDYmw1O1Q4cO5Y033qBGjRq4uLikPf+s6zzUpsWpWq1ocVrVlm4kpaWEK/tVz3T2qKd6pi1Scqo2U9d5pL1Ip2P37t2Z3phS0jykeYA0Dy1pcp3HqlWrKFiw4GPPnT59OtMbEkK8XJ56zCM2NpbY2Fj69OlDXFwcsbGxxMXFcfPmzcfOwAghXk1PfecxfPhwwsLCgNQLw9JeYDDQrFkz7SsTQmRrGR7zGDlyJFOnTs2qetIlxzzkmAfIMQ8taXLANDuQ5iHNA6R5aElJ83hpbr2g1cRomcqe/Sd8f/7lt7zTrht+7/XH773+DB/z8J3ynfi7+Hb7kBN//5tt6tU6N9tMT7cFWk2MlqnstjHh+8/jJ/l8fCDrlgaxbmkQX0wcCcAv4b/RufcQIqMuZat6tczNVtPTldq/f3/aIKCNGzcyYcIE1q1bp8m2tJoYLVPZs/+Eb6PRyN+nz7J45Vp8u37IkFGTiL56HYCVazcx9bMR5Mtr3T1WXtV9mxFNmsfkyZNZuHAhSUlJzJkzh02bNlGqVCl++uknJk2apPr2tJoYLVPZs/+E7+s3b1GrWmUG9unG+mXzqVS+LAMDx2OxWFg4axIVy5VRXKcW9Wqdm5XT0zUZURUeHs6mTZvQ6/Xs27eP4OBgHBwc6NChA97e3qpvT6uJ0bY06dyW9oGamUU8CrLgi4lpj3t09mPhklVcjr5GEY+Cz3jl83tV922G21I9EXByciImJnUCdsGCBbl//z4ACQkJGAzq9yutJkbb0qRzW9oHamb+c+Y8m358fKmExQIGg15xfU96VfdtRjRpHv3798ff35/p06dTpEgRunbtypQpUwgICKBHjx6qb0+ridG2NOnclvaBmpl2djqmzfmaS1euAhC8YStvlCpBwfz5rKpRq3q1zs1W09OV8PLyonTp0uzatYsLFy5QpUoVcuTIwbRp06hUqZLq29NqYrQtTTq3pX2gZmbp/xVn5NAPGfDxOExmMwXyufP5uE+sqk/LerXOzVbT07MDuUhMLhIDuUhMS6/0RWJCiKwlzUMIoYg0DyGEItI8hBCKSPMQQigizUMIoYicqn2F2doNtLVw99cFmuTmfPtDTXK1IqdqhRBZRpqHEEIRaR5CCEWkeQghFJHmIYRQ5KVpHjKgVvvBt2oObLaVfbDn0Ak8e4wGYNqSjQQEzkr707DPOPw//iJb1atVZnpeiuYhA2q1HXyr9sBmW9kHF6JvMGvllrTJXIHd2xIybRgh04Yxe3h3HOwNTOrXMdvUq1Xm02jSPCZNmkRcXJwW0emSAbXaDr5Ve2CzLeyDhCQjo4J+4KN3W6f79QnfrKVrq/qULa78GiRb+veVHk2ax8aNGwkICGDnzqyZuiUDarUdfKv2wGZb2AcTv1uHf+O3Kf16of987cCfp4iOiaVz87rZpl4tM59Gk+ZRpEgRgoKCWLZsGe3bt2fbtm0kJiZqsSlABtRqmauF7L4PgneGo9fb4duoZrpfX77tF3r5NEJvZ92Pjy39+0p3W6onAjqdjlKlSrFixQqGDh3Kjh07aNy4MV26dGH48OGqb08G1MqwZjVzQ385zF9nLxIQOIsB0xeRZEwmIHAW12/FcevOXU6cuUiTWpWtqlXNerXOfBpNmsejna927drMnTuXvXv3MnLkSN555x3VtycDamVYs5q5qyYNYv3nHxEybRhffdILRwd7QqYNI79bHv78J5LyJYvg4uSQberVOvNpNFkZ1aVLl/88Z29vT4UKFahQoYLq25MBtTKsWcvcR0VdvYlHPuvuQPeALf37So+sqn2FyapaWVX7gKyqFUJkGWkeQghFpHkIIRSR5iGEUESahxBCEWkeQghFXtlTtW+6FVM9E+DkrSjVM23tlKot1ZvLwVn1TIBLI2trkptn/G5NcuVUrRAiy0jzEEIoIs1DCKGINA8hhCLSPIQQikjzEEIo8tI0Dy0mRpcuW5JF64MI/mkpP+z4nnKVyqhQqW1NOQdt67WVWgM6+LA/YjO/hG9ix64QqlRVNlrCUOMdnPtMwbn3FBzbDwGXXKDT4dD8PZz7TMW5z1QcGisfqgwyPT1TtJgY7eTsyNfBc1gctIIOTd5j4azFTAsany1rfUDtKeegXb22VGup0iUYP/kT/Nv2pH7tNsycEcTyVfMznWNXsDj2tVqQsHQiCd+OwnLrKg4N/DBUrINd3kIkfDuKhO9GY1eFysVUAAATi0lEQVSsLPqybymq1eanpwNERERw5MgRAL7//nv69u3LV199hdFoVH1bWkyM9mxQi4uRlzmwOwKAvTv281GfT7NlrQ+oPeUctKvXlmpNSjIyuP8orl27AcCRI8fJX8Ade3v7TOWYr0aSsOBjSEoAvT26XK9hSbgLOjuwdwS9PegNqX9MyYpqzcrp6ZpcCjhjxgwOHz5MSkoKRYoUQafT0alTJ/bs2cOECROYNGmSqtt71sTo+Pi7ijKLlyxKzI0Yxs0aRZnypYiPu8vsiV9ly1ofGDp0LACNG9ezKudRWtVrS7VejLrMxaiHV2BOnvop27ftITlZwQ+42YT+jWo4tuqFxZSMcd96LLHX0ZericuguWBnh+n8CUyn/1RUq5b/vp6kSfPYv38/oaGhGI1GGjZsyP79+7G3t6d+/fr4+Piovj0tJkYbDAbqetXmfb/+HD9ykobN6hG0chbNaviSbFT2W0GrWrVkS/VqXauLizNBC2dQuHAh/H17KM4x/fsH9//9A0OVhjh1GkHKiXC4H8/9OQPA3gEn/8EYajUn5eCPmc62+enpFouF+Ph4bt++TUJCAnfvpna8xMREZd06A1pMjL5+7SbnT0dy/MhJIPVji53ejiKve2S7WrVkS/VqWWuRIoXYsTsEs8lEm5ZduBMXn+kM3Wv5sSvyRtrjlKP70OVxx1CuJslHfwGzCZISSDl+AP3rbyqq0+anp/fu3ZumTZvi4+PDiBEj6NmzJzNmzKBz5874+fmpvj0tJkYf2B1B4WIeaWdYqr9dBSwWLkdFZ7tatWRL9WpVa86cOdi8fSWbN+2kV/chJCYmKcrR5XTF0bcfOKee/TBUqI35xiVMV85hKPf/94ix06MvXQ3z5TOKtmHz09N9fHxo1qwZJpOJHDly8NZbb3HgwAE++ugj6tSpo/r2tJgYHXPjFkN6fMLoaSNwdnHCmJTM0J4jMSZZd8DXlqacg23Vq1WtvT/oStFihfFu3QTv1k3Snvfx7sbtW7HPnWO++C/JYZtwfncUFosJS3wsSWvmYDEm4tisK84fTAOLGVPkSZIjlB1IlunpT5Al+bazxB1sq15Zkp9KluQLIbKMNA8hhCLSPIQQikjzEEIoIs1DCKGINA8hhCLanFOzARfv3njRJTy3/C55NMm9fj9Ok1xbutG1oyFzi9ueV8OgSE1yo2q8kfE3ZRF55yGEUESahxBCEWkeQghFpHkIIRSR5iGEUESahxBCkZemeWgxMVqtidlP0nq6ddOWjfjrQoSqmaDupHOt9oFWuT17d2ZfxGb2hm9iyaqvcHd3szqzpX8zVv60KO3Pxl+DibiwBzf31zKd5eLXlnwrFpNvxfe8Nn0Sdq+5gp0duQf3J98PS8kfsgKXtq2trvlRL0Xz0GJitFoTs7Oi1kcV/18xPh0/HB061TLVnnSu1T7QKrdS5Tf5cGBPvJt1omHtNpw/e4GPPx1kde62tTvo0qQXXZr0oluLPsRcj2HGp3O4dfN2pnLsy7xBzs4duPnBAG682xPTxUvk6t0Tl7atMRQtwo13e3CjV19ydPDHvlxZq+t+QLPmsWvXLgIDA+nVqxd9+/ZlypQpadPU1abFxGi1JmZnRa0PODk7MefrqUwcM1OVvAfUnnSu1T7QKvfY0ZN4VmtO/J27ODo6ULBQgUwNAXoe7/Xvwu2YWDas2JTp1yb/8y/XA97Fcu8eONhjl88dc9wdnOvX5f7WH8FkxhJ/l8Rde3Bu3iTjwOekSfNYuHAh69ato1KlSuh0OqpUqUKBAgUYNWoUISEhqm/vWROjlboYdZmdO/amPbZqYvYjtKj1gamzxrBqyRpO/fWv1VmPGjp0LMHBoarlabUPtNy3KSkpNG/VmD9O7uXt2jVYvXKD1ZkP5HHLQ5cPOjD7sy+Vh5hMONWvQ4GNa3CsUon7W7djVyA/puvXH37L9Rvo87mrUHEqTZrHtm3bmD9/Pp07dyYoKIjw8HB69epFSEgIixcvVn17Wk6MdnFxZvHyLynxv9cZ1H+k1Xla1dq1ZwdSUkyErNpoVU5W0GofaD05/MetuylfsjYzpwWxev236HTqfDT07dKaX3YcsHo+buIvYVxr2Zb4RUvJO3tGan2P7g6dDsxm64p9hCbNIykpiYSE1GnNiYmJxMamvsVzcXHBzk79TWo1MVqNidlP0qpW/05tqFy1Atv2hrAkOAgnZ0e27Q0hf8F81pasOq32gVa5xUsUo+bb1dIe/7BiHUWKeuDqqs6aoyZtvNgcvE3x6/WFPXCo9PBg/v0t29EXLIDpxk307nnTnrdzz4vpunprujRpHu3ataNTp058/vnndOvWjXbt2nHlyhX8/Pzw9vZWfXtaTIxWa2L2k7Sabu3TpAtN67ajZcMAunfoT2JCEi0bBnD9avZbAKjVPtAqt0DBfHy96Avc3FwB8Atozam/T3P7tvXHPXLlyUnREoU5eviE4gy9e15emzAWuzy5AXBu+g4p5yJJ3LcfF+8WoLdDlzMHzu94kfhLmNU1P6DJqto+ffpQsWJFTp48SWBgIJ6enty7d4/p06dTpow6N4t+lBYTo9WamJ0VtdoarfaBVrkHI35n7hcLWb9lGSmmFK5F36BHlwFW5wIULV6Em9diMKUo/2hlPHqc+KUryBs0B1JMmG7e5FbgaEzXr6Mv7EG+pYvQ2Ru4t3Ezxj+PqlI3vMLT07Wamh1vVP/mOoVz5c34mxSQJfng7pJbk9yiLtp8XAwtrtck1yP850y/5qW4zkMIkfWkeQghFJHmIYRQRJqHEEIRaR5CCEWkeQghFLGJU7VCiOxH3nkIIRSR5iGEUESahxBCEWkeQghFpHkIIRSR5iGEUESahxBCEWkeQghFpHkIIRR5aZrH5s2badmyJU2bNmXlypWq5d69exdvb28uXbqkWuZXX31Fq1ataNWqFTNmzFAtd+7cubRs2ZJWrVppMmh6+vTpBAYGqpbXtWtXWrVqhY+PDz4+Phw9av2Uqz179tCuXTtatGjBpEmTVKgS1qxZk1ajj48P1atXZ8KECapkh4aGpv1bmD59uiqZAN988w3NmjWjdevWLFiwQLXcx1heAlevXrU0atTIcvv2bcu9e/csrVu3tpw+fdrq3D///NPi7e1tKV++vOXixYsqVGqxhIWFWTp06GBJSkqyGI1GS7du3Sw7d+60OvfgwYOWjh07WpKTky0JCQmWRo0aWc6ePatCxanCw8MttWrVsnzyySeq5JnNZkvdunUtycnJquRZLBZLVFSUpW7dupbo6GiL0Wi0dOrUybJ3717V8i0Wi+Xff/+1NGnSxBITE2N11v379y1vvfWWJSYmxpKcnGzx9/e3hIWFWZ0bFhZm8fb2tsTHx1tSUlIsH3zwgWXHjh1W5z7ppXjnER4ezttvv42rqysuLi40a9aMH3/80erckJAQPvvsM/Lnz69Clany5ctHYGAgDg4O2NvbU7JkSa5cuZLxCzNQs2ZNli1bhsFgICYmBpPJhIuLiwoVQ2xsLLNnz6Zv376q5AGcO3cOgJ49e9KmTRtWrFhhdeZPP/1Ey5YtKViwIPb29syePZvKlStbnfuocePGMXToUNzcrL/dpMlkwmw2k5CQQEpKCikpKTg6Olqde/LkSerWrUvOnDnR6/XUq1ePXbt2WZ37pJeieVy/fp18+R7OjMyfPz/Xrl2zOnfy5MnUqFHD6pxHlS5dmipVqgAQGRnJ9u3badCggSrZ9vb2zJs3j1atWuHp6UmBAgUyftFzGDt2LEOHDiV3bvXmfd65cwdPT0+CgoJYsmQJq1evJizMusneFy5cwGQy0bdvX3x8fFi1ahV58qhzewRI/SWVmJhIixYtVMnLmTMngwcPpkWLFjRo0IDChQtTrVq1jF+YgfLly3PgwAFiY2NJSkpiz5493Lx5U4WKH/dSNA+z2fzYDXgsFotqN+TRyunTp+nZsycff/wxxYsXVy130KBBREREEB0drcrd+dasWUOhQoXw9PRUobqHqlatyowZM8iVKxdubm74+/uzb98+qzJNJhMRERFMmTKF4OBgjh07xoYN6t3ZbfXq1fTo0UO1vFOnTrFu3Tp+/vln9u/fj52dHYsWLbI619PTk3bt2tG1a1fef/99qlevbvVtUtPzUjSPggULcuPGw/uT3LhxQ9WPGmr7/fff6d69O8OHD8fXV5371J49e5a///4bAGdnZ5o2bco///xjde62bdsICwvDx8eHefPmsWfPHqZMmWJ17uHDh4mIiEh7bLFYMBisuxOIu7s7np6euLm54eTkxDvvvMOxY8esLRUAo9HIoUOH8PLyUiUP4MCBA3h6epI3b14cHBxo164dv/32m9W5d+/epWnTpmzevJnly5fj4OBA0aJFVaj4cS9F86hduzYRERHcunWLhIQEdu7cSf369V90WemKjo6mf//+zJw5k1atWqmWe+nSJUaPHo3RaMRoNLJ7926qV69ude7ixYvZsmULoaGhDBo0CC8vL0aNsv7O8/Hx8cyYMYOkpCTu3r3Lhg0baNLEupswN2rUiAMHDnDnzh1MJhP79++nfPnyVtcK8M8//1C8eHHVjiMBlC1blvDwcO7fv4/FYmHPnj1UrFjR6txLly7Rr18/UlJSiI+PZ+3atap91HqUJjd9ymoFChRg6NChdOvWjeTkZPz9/alUqdKLLitdixYtIikpiWnTpqU917FjRzp16mRVboMGDTh27Bht27ZFr9fTtGlTVZuT2ho1asTRo0dp27YtZrOZzp07U7VqVasyK1euzPvvv0/nzp1JTk6mTp06+Pn5qVLvxYsXKViwoCpZD9StW5eTJ0/Srl077O3tqVixIn369LE6t2zZsjRt2pQ2bdpgMpno3r27Kr9IniSTxIQQirwUH1uEEFlPmocQQhFpHkIIRaR5CCEUkeYhhFBEmodIU7VqVS5dusTx48cZNGjQM7/32LFjjB07NtPbmDBhAl9++aXSEh/j5eXF8ePHVckSmSfNQ/xHxYoVmTdv3jO/58yZM6qsHxK2S5qHDTp48CDt27dn8ODBtG7dmvbt23P27FkAAgMD6du3L61ateLzzz/HaDQyZcoUfH19adOmDYGBgdy9exdIvUTcx8eHtm3bMmbMGMxmc1q+t7c3APfu3WPkyJE0a9aMli1bMmvWLKKjo5k3bx6HDx9m5MiRQOocjfbt29O2bVs6duzIkSNHgNRLpQcPHkyzZs3o2rVr2mraR5lMJho0aMCJEyfSnhsyZAirVq3i5s2b9OvXjw4dOuDl5UXXrl2JiYn5z/54UG96jxcsWICvry8+Pj7069dPmp5KpHnYqBMnTtC1a1c2b95Mu3btGDFiRNrXEhMT2bp1KyNGjOCbb75Br9ezfv16Nm3aRP78+Zk5cyZGo5HBgwcTGBjIxo0bqVWrFomJif/Zzrx580hKSmLbtm1s3LiRP/74g6ioKAYNGkSNGjWYOnUqkZGRzJ49m2+++YaNGzcyceJEBg4cyP3795k3bx5OTk78+OOPzJ07l/Pnz/9nG3q9Hj8/P9avXw9AXFwcERERtG7dmq1bt1KlShWCg4PZvXs3Tk5OhIaGPvd+2rhxI//++y9r1qwhNDSUBg0aMHr0aAV7XDzppbg8/VVUtmzZtHEBfn5+TJgwgdu3bwM8diny3r17iY+PJzw8HIDk5GTy5s3Lv//+i8FgSFst6+3tne4xjPDwcEaOHIler0ev16fN3Xjwgw4QFhbG9evX6d69e9pzOp2OqKgoIiIiGDVqFDqdDjc3t6euX/Hz88Pf35/AwEC2bNmCl5cXuXLl4r333uPw4cMsXryYyMhITp8+nakZHT///DPHjx9Pu0z9wfwMYT1pHjZKr9c/9blHF2+ZzWZGjRqVNjPk3r17JCUlceXKFZ5cmZDeqlaDwfDYeIPo6GicnJwe+x6z2Yynpydz5sx57PserGx+dDvp1Q1QuHBh3nzzTfbu3cv69evTFt99/vnnHDt2DD8/P2rVqkVKSsp/6tbpdI89l5yc/FhtD9a7QOrq2Li4uHRrEJkjH1ts1KlTpzh16hQAwcHBVK1aNd1hPXXr1mXlypUYjUbMZjNjxoxh1qxZlClTBovFkjZDY/fu3en+UHl6erJhwwbMZjNGo5FBgwZx6NAh9Ho9KSkpad8TFhaWdtxl3759tGnThsTEROrVq8fatWsxm83ExcWxe/fup/6dAgIC+Pbbb0lISEh793TgwAHee+892rZtS968eQkPD8dkMj32Ojc3N65cuUJMTAwWi4WtW7c+9vdfu3Zt2nGeuXPn8vHHHz/3fhZPJ+88bJS7uztz5szh8uXLuLm5PXWQcr9+/Zg+fTq+vr6YTCbKlStHYGAg9vb2BAUFMW7cOGbNmkW5cuXImzfvf14/YMAAJk+ejI+PDyaTKW3I9IULFwgKCmLAgAF89dVXTJgwgWHDhqXN5ViwYAE5cuRg4MCBfPbZZ7Ro0QI3NzfeeOONp/6dvLy8GD9+PL179057rn///syYMYO5c+dib29PtWrViIqKeux1pUqVomPHjvj5+ZEvXz4aNmyYdgq3ffv2XLt2jYCAAHQ6HYUKFXpsRbNQTlbV2qCDBw8yceJEtmzZ8qJLEa8w+dgihFBE3nkIIRSRdx5CCEWkeQghFJHmIYRQRJqHEEIRaR5CCEWkeQghFPk/8DxDGrjHEt8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "mat = confusion_matrix(ytest, y_model)\n",
    "\n",
    "sns.heatmap(mat, square=True, annot=True, cbar=False)\n",
    "plt.xlabel('predicted value')\n",
    "plt.ylabel('true value');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This shows us where the mis-labeled points tend to be: for example, a large number of twos here are mis-classified as either ones or eights.\n",
    "Another way to gain intuition into the characteristics of the model is to plot the inputs again, with their predicted labels.\n",
    "We'll use green for correct labels, and red for incorrect labels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 100 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(10, 10, figsize=(8, 8),\n",
    "                         subplot_kw={'xticks':[], 'yticks':[]},\n",
    "                         gridspec_kw=dict(hspace=0.1, wspace=0.1))\n",
    "\n",
    "test_images = Xtest.reshape(-1, 8, 8)\n",
    "\n",
    "for i, ax in enumerate(axes.flat):\n",
    "    ax.imshow(test_images[i], cmap='binary', interpolation='nearest')\n",
    "    ax.text(0.05, 0.05, str(y_model[i]),\n",
    "            transform=ax.transAxes,\n",
    "            color='green' if (ytest[i] == y_model[i]) else 'red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Examining this subset of the data, we can gain insight regarding where the algorithm might be not performing optimally.\n",
    "To go beyond our 80% classification rate, we might move to a more sophisticated algorithm such as support vector machines (see [In-Depth: Support Vector Machines](05.07-Support-Vector-Machines.ipynb)), random forests (see [In-Depth: Decision Trees and Random Forests](05.08-Random-Forests.ipynb)) or another classification approach."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section we have covered the essential features of the Scikit-Learn data representation, and the estimator API.\n",
    "Regardless of the type of estimator, the same import/instantiate/fit/predict pattern holds.\n",
    "Armed with this information about the estimator API, you can explore the Scikit-Learn documentation and begin trying out various models on your data.\n",
    "\n",
    "In the next section, we will explore perhaps the most important topic in machine learning: how to select and validate your model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--NAVIGATION-->\n",
    "< [What Is Machine Learning?](05.01-What-Is-Machine-Learning.ipynb) | [Contents](Index.ipynb) | [Hyperparameters and Model Validation](05.03-Hyperparameters-and-Model-Validation.ipynb) >\n",
    "\n",
    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/05.02-Introducing-Scikit-Learn.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
